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| author | jaseg <git@jaseg.net> | 2020-10-14 12:49:08 +0200 |
|---|---|---|
| committer | jaseg <git@jaseg.net> | 2020-10-14 12:49:08 +0200 |
| commit | 3559d845d29272050d4d44e18e0bb84e676e48ff (patch) | |
| tree | d8d6ce99e40b1a3745d76f0c9aac2f10e7286481 /fw/hid-dials/Drivers/CMSIS/NN | |
| parent | 491b2acaaa9db1798445f3e174e68ada7424fc33 (diff) | |
| parent | 6ab94e0b318884bbcb95e2ea3835f951502e1d99 (diff) | |
| download | minikbd-3559d845d29272050d4d44e18e0bb84e676e48ff.tar.gz minikbd-3559d845d29272050d4d44e18e0bb84e676e48ff.tar.bz2 minikbd-3559d845d29272050d4d44e18e0bb84e676e48ff.zip | |
Merge firmware and pcb repos
Diffstat (limited to 'fw/hid-dials/Drivers/CMSIS/NN')
73 files changed, 11827 insertions, 0 deletions
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/Compiler/EventRecorderConf.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/Compiler/EventRecorderConf.h new file mode 100644 index 0000000..5958233 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/Compiler/EventRecorderConf.h @@ -0,0 +1,44 @@ +/*------------------------------------------------------------------------------
+ * MDK - Component ::Event Recorder
+ * Copyright (c) 2016 ARM Germany GmbH. All rights reserved.
+ *------------------------------------------------------------------------------
+ * Name: EventRecorderConf.h
+ * Purpose: Event Recorder Configuration
+ * Rev.: V1.0.0
+ *----------------------------------------------------------------------------*/
+
+//-------- <<< Use Configuration Wizard in Context Menu >>> --------------------
+
+// <h>Event Recorder
+
+// <o>Number of Records
+// <8=>8 <16=>16 <32=>32 <64=>64 <128=>128 <256=>256 <512=>512 <1024=>1024
+// <2048=>2048 <4096=>4096 <8192=>8192 <16384=>16384 <32768=>32768
+// <65536=>65536 <131072=>131072 <262144=>262144 <524288=>524288
+// <1048576=>1048576
+// <i>Configure size of Event Record Buffer (each record is 16 bytes)
+// <i>Must be 2^n (min=8, max=1048576)
+#define EVENT_RECORD_COUNT 64U
+
+// <o>Time Stamp Source
+// <0=> DWT Cycle Counter <1=> SysTick
+// <3=> User Timer (Normal Reset) <4=> User Timer (Power-On Reset)
+// <i>Selects source for 32-bit time stamp
+#define EVENT_TIMESTAMP_SOURCE 1
+
+// <h>SysTick Configuration
+// <i>Configure values when Time Stamp Source is set to SysTick
+
+// <o>SysTick Input Clock Frequency [Hz] <1-1000000000>
+// <i>Defines SysTick input clock (typical identical with processor clock)
+#define SYSTICK_CLOCK 100000000U
+
+// <o>SysTick Interrupt Period [us] <1-1000000000>
+// <i>Defines time period of the SysTick timer interrupt
+#define SYSTICK_PERIOD_US 1000U
+
+// </h>
+
+// </h>
+
+//------------- <<< end of configuration section >>> ---------------------------
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM0/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM0/RTE_Components.h new file mode 100644 index 0000000..182fb76 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM0/RTE_Components.h @@ -0,0 +1,24 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10'
+ * Target: 'ARMCM0'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+#define RTE_Compiler_EventRecorder
+ #define RTE_Compiler_EventRecorder_DAP
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_EVR /* Compiler I/O: STDOUT EVR */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM3/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM3/RTE_Components.h new file mode 100644 index 0000000..2aa5627 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM3/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10'
+ * Target: 'ARMCM3'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM4_FP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM4_FP/RTE_Components.h new file mode 100644 index 0000000..bdf6fb9 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM4_FP/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10'
+ * Target: 'ARMCM4_FP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM7_SP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM7_SP/RTE_Components.h new file mode 100644 index 0000000..9d43970 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM7_SP/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10'
+ * Target: 'ARMCM7_SP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp new file mode 100644 index 0000000..ee1083b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp @@ -0,0 +1,196 @@ +/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project: CMSIS NN Library
+* Title: arm_nnexamples_cifar10.cpp
+*
+* Description: Convolutional Neural Network Example
+*
+* Target Processor: Cortex-M4/Cortex-M7
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+* - Redistributions of source code must retain the above copyright
+* notice, this list of conditions and the following disclaimer.
+* - Redistributions in binary form must reproduce the above copyright
+* notice, this list of conditions and the following disclaimer in
+* the documentation and/or other materials provided with the
+* distribution.
+* - Neither the name of Arm LIMITED nor the names of its contributors
+* may be used to endorse or promote products derived from this
+* software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup CNNExample Convolutional Neural Network Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates a convolutional neural network (CNN) example with the use of convolution,
+ * ReLU activation, pooling and fully-connected functions.
+ *
+ * \par Model definition:
+ * \par
+ * The CNN used in this example is based on CIFAR-10 example from Caffe [1].
+ * The neural network consists
+ * of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a
+ * fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will
+ * be classified into one of the 10 output classes.
+ * This example model implementation needs 32.3 KB to store weights, 40 KB for activations and
+ * 3.1 KB for storing the \c im2col data.
+ *
+ * \image html CIFAR10_CNN.gif "Neural Network model definition"
+ *
+ * \par Variables Description:
+ * \par
+ * \li \c conv1_wt, \c conv2_wt, \c conv3_wt are convolution layer weight matrices
+ * \li \c conv1_bias, \c conv2_bias, \c conv3_bias are convolution layer bias arrays
+ * \li \c ip1_wt, ip1_bias point to fully-connected layer weights and biases
+ * \li \c input_data points to the input image data
+ * \li \c output_data points to the classification output
+ * \li \c col_buffer is a buffer to store the \c im2col output
+ * \li \c scratch_buffer is used to store the activation data (intermediate layer outputs)
+ *
+ * \par CMSIS DSP Software Library Functions Used:
+ * \par
+ * - arm_convolve_HWC_q7_RGB()
+ * - arm_convolve_HWC_q7_fast()
+ * - arm_relu_q7()
+ * - arm_maxpool_q7_HWC()
+ * - arm_avepool_q7_HWC()
+ * - arm_fully_connected_q7_opt()
+ * - arm_fully_connected_q7()
+ *
+ * <b> Refer </b>
+ * \link arm_nnexamples_cifar10.cpp \endlink
+ *
+ * \par [1] https://github.com/BVLC/caffe
+ */
+
+#include <stdint.h>
+#include <stdio.h>
+#include "arm_math.h"
+#include "arm_nnexamples_cifar10_parameter.h"
+#include "arm_nnexamples_cifar10_weights.h"
+
+#include "arm_nnfunctions.h"
+#include "arm_nnexamples_cifar10_inputs.h"
+
+#ifdef _RTE_
+#include "RTE_Components.h"
+#ifdef RTE_Compiler_EventRecorder
+#include "EventRecorder.h"
+#endif
+#endif
+
+// include the input and weights
+
+static q7_t conv1_wt[CONV1_IM_CH * CONV1_KER_DIM * CONV1_KER_DIM * CONV1_OUT_CH] = CONV1_WT;
+static q7_t conv1_bias[CONV1_OUT_CH] = CONV1_BIAS;
+
+static q7_t conv2_wt[CONV2_IM_CH * CONV2_KER_DIM * CONV2_KER_DIM * CONV2_OUT_CH] = CONV2_WT;
+static q7_t conv2_bias[CONV2_OUT_CH] = CONV2_BIAS;
+
+static q7_t conv3_wt[CONV3_IM_CH * CONV3_KER_DIM * CONV3_KER_DIM * CONV3_OUT_CH] = CONV3_WT;
+static q7_t conv3_bias[CONV3_OUT_CH] = CONV3_BIAS;
+
+static q7_t ip1_wt[IP1_DIM * IP1_OUT] = IP1_WT;
+static q7_t ip1_bias[IP1_OUT] = IP1_BIAS;
+
+/* Here the image_data should be the raw uint8 type RGB image in [RGB, RGB, RGB ... RGB] format */
+uint8_t image_data[CONV1_IM_CH * CONV1_IM_DIM * CONV1_IM_DIM] = IMG_DATA;
+q7_t output_data[IP1_OUT];
+
+//vector buffer: max(im2col buffer,average pool buffer, fully connected buffer)
+q7_t col_buffer[2 * 5 * 5 * 32 * 2];
+
+q7_t scratch_buffer[32 * 32 * 10 * 4];
+
+int main()
+{
+ #ifdef RTE_Compiler_EventRecorder
+ EventRecorderInitialize (EventRecordAll, 1); // initialize and start Event Recorder
+ #endif
+
+ printf("start execution\n");
+ /* start the execution */
+
+ q7_t *img_buffer1 = scratch_buffer;
+ q7_t *img_buffer2 = img_buffer1 + 32 * 32 * 32;
+
+ /* input pre-processing */
+ int mean_data[3] = INPUT_MEAN_SHIFT;
+ unsigned int scale_data[3] = INPUT_RIGHT_SHIFT;
+ for (int i=0;i<32*32*3; i+=3) {
+ img_buffer2[i] = (q7_t)__SSAT( ((((int)image_data[i] - mean_data[0])<<7) + (0x1<<(scale_data[0]-1)))
+ >> scale_data[0], 8);
+ img_buffer2[i+1] = (q7_t)__SSAT( ((((int)image_data[i+1] - mean_data[1])<<7) + (0x1<<(scale_data[1]-1)))
+ >> scale_data[1], 8);
+ img_buffer2[i+2] = (q7_t)__SSAT( ((((int)image_data[i+2] - mean_data[2])<<7) + (0x1<<(scale_data[2]-1)))
+ >> scale_data[2], 8);
+ }
+
+ // conv1 img_buffer2 -> img_buffer1
+ arm_convolve_HWC_q7_RGB(img_buffer2, CONV1_IM_DIM, CONV1_IM_CH, conv1_wt, CONV1_OUT_CH, CONV1_KER_DIM, CONV1_PADDING,
+ CONV1_STRIDE, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_OUT_RSHIFT, img_buffer1, CONV1_OUT_DIM,
+ (q15_t *) col_buffer, NULL);
+
+ arm_relu_q7(img_buffer1, CONV1_OUT_DIM * CONV1_OUT_DIM * CONV1_OUT_CH);
+
+ // pool1 img_buffer1 -> img_buffer2
+ arm_maxpool_q7_HWC(img_buffer1, CONV1_OUT_DIM, CONV1_OUT_CH, POOL1_KER_DIM,
+ POOL1_PADDING, POOL1_STRIDE, POOL1_OUT_DIM, NULL, img_buffer2);
+
+ // conv2 img_buffer2 -> img_buffer1
+ arm_convolve_HWC_q7_fast(img_buffer2, CONV2_IM_DIM, CONV2_IM_CH, conv2_wt, CONV2_OUT_CH, CONV2_KER_DIM,
+ CONV2_PADDING, CONV2_STRIDE, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_OUT_RSHIFT, img_buffer1,
+ CONV2_OUT_DIM, (q15_t *) col_buffer, NULL);
+
+ arm_relu_q7(img_buffer1, CONV2_OUT_DIM * CONV2_OUT_DIM * CONV2_OUT_CH);
+
+ // pool2 img_buffer1 -> img_buffer2
+ arm_maxpool_q7_HWC(img_buffer1, CONV2_OUT_DIM, CONV2_OUT_CH, POOL2_KER_DIM,
+ POOL2_PADDING, POOL2_STRIDE, POOL2_OUT_DIM, col_buffer, img_buffer2);
+
+// conv3 img_buffer2 -> img_buffer1
+ arm_convolve_HWC_q7_fast(img_buffer2, CONV3_IM_DIM, CONV3_IM_CH, conv3_wt, CONV3_OUT_CH, CONV3_KER_DIM,
+ CONV3_PADDING, CONV3_STRIDE, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_OUT_RSHIFT, img_buffer1,
+ CONV3_OUT_DIM, (q15_t *) col_buffer, NULL);
+
+ arm_relu_q7(img_buffer1, CONV3_OUT_DIM * CONV3_OUT_DIM * CONV3_OUT_CH);
+
+ // pool3 img_buffer-> img_buffer2
+ arm_maxpool_q7_HWC(img_buffer1, CONV3_OUT_DIM, CONV3_OUT_CH, POOL3_KER_DIM,
+ POOL3_PADDING, POOL3_STRIDE, POOL3_OUT_DIM, col_buffer, img_buffer2);
+
+ arm_fully_connected_q7_opt(img_buffer2, ip1_wt, IP1_DIM, IP1_OUT, IP1_BIAS_LSHIFT, IP1_OUT_RSHIFT, ip1_bias,
+ output_data, (q15_t *) img_buffer1);
+
+ arm_softmax_q7(output_data, 10, output_data);
+
+ for (int i = 0; i < 10; i++)
+ {
+ printf("%d: %d\n", i, output_data[i]);
+ }
+
+ return 0;
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_inputs.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_inputs.h new file mode 100644 index 0000000..4ff8dc2 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_inputs.h @@ -0,0 +1,6 @@ +/* Here are two different test images */
+
+//#define IMG_DATA {158,112,49,159,111,47,165,116,51,166,118,53,160,112,46,156,109,41,162,115,47,159,113,45,158,111,44,159,113,41,161,116,41,160,111,52,161,111,49,166,117,41,169,117,45,170,119,44,167,117,40,162,113,38,160,111,39,160,112,43,156,109,44,149,107,45,150,107,45,148,106,43,149,107,44,143,101,39,140,98,43,141,97,41,143,97,38,137,95,36,126,91,36,116,85,33,152,112,51,151,110,40,159,114,45,166,116,56,162,112,49,160,113,43,164,117,47,162,114,45,163,116,46,156,110,38,155,111,41,159,110,54,163,113,52,170,119,41,171,117,40,171,115,33,169,115,30,160,111,33,154,112,41,151,115,50,145,110,53,139,104,55,140,102,52,141,100,48,149,105,50,147,102,46,145,102,45,142,97,38,143,98,34,136,95,31,125,91,32,119,88,34,151,110,47,151,109,33,158,111,36,167,111,48,160,106,42,163,115,44,165,117,45,165,117,45,163,115,43,162,115,43,158,114,48,157,109,57,161,111,51,166,115,38,167,114,37,169,113,35,170,116,39,159,114,47,145,111,54,121,96,49,110,90,52,98,78,50,101,77,47,114,85,50,120,86,48,134,96,55,143,103,51,140,99,39,142,99,35,139,98,34,130,95,34,120,89,33,155,107,40,155,110,32,160,109,31,174,112,44,167,110,43,167,117,46,169,120,48,169,119,48,165,115,44,165,117,45,167,123,57,191,146,95,177,130,75,157,111,41,162,115,47,164,114,54,158,112,58,149,111,67,104,80,47,103,87,65,98,90,76,92,90,84,80,75,66,74,63,50,86,70,52,83,62,39,113,85,45,132,98,46,140,102,43,140,101,39,136,99,39,127,94,36,155,107,41,156,114,48,161,115,49,170,114,47,169,114,43,163,113,40,169,120,47,166,116,44,164,113,41,164,116,42,173,128,59,246,214,164,195,156,107,151,114,56,146,111,60,142,108,71,111,80,50,78,53,31,85,69,56,113,103,98,112,110,111,106,114,118,97,102,105,93,94,93,74,72,67,84,78,70,85,73,47,105,83,45,128,96,48,138,101,46,133,94,36,129,93,36,148,109,54,133,104,64,130,100,57,147,112,53,161,115,44,165,113,39,167,116,41,167,115,41,163,111,37,165,116,39,163,118,42,180,138,85,157,122,78,128,102,58,97,75,43,66,50,31,69,58,43,66,56,45,89,83,76,118,113,110,122,121,120,119,122,122,114,116,116,94,96,96,99,100,97,91,91,86,58,58,47,67,58,37,108,84,49,140,105,58,138,98,44,134,95,40,127,100,57,109,95,80,47,37,17,88,74,28,153,117,48,170,118,43,168,115,40,170,118,43,169,117,42,166,116,37,164,120,39,147,107,52,129,98,59,127,108,75,100,87,70,68,67,57,78,83,72,72,75,64,83,84,74,132,130,121,146,142,132,124,118,108,105,99,90,107,102,94,115,111,103,85,83,77,63,71,69,46,47,39,79,61,36,132,98,58,141,99,48,134,93,39,131,115,90,99,96,92,42,43,38,70,64,41,143,111,56,167,117,42,165,114,36,168,116,39,171,119,49,161,113,51,140,109,51,120,94,49,130,110,77,144,131,107,116,106,93,88,87,79,91,95,88,85,88,82,77,77,69,124,118,107,163,153,140,136,124,112,102,93,81,106,98,88,100,93,84,85,81,74,54,60,58,49,53,49,57,47,32,107,83,50,138,103,51,136,97,39,170,161,144,103,105,105,54,58,59,124,121,113,153,124,82,161,113,43,163,117,41,166,122,50,165,121,66,174,135,95,113,89,59,125,105,78,157,141,121,156,143,128,121,111,101,86,80,74,82,81,77,84,85,82,80,78,73,81,71,61,138,125,112,146,135,123,113,103,93,87,79,70,83,77,69,86,82,76,71,73,67,56,57,53,40,35,27,74,59,35,133,106,59,137,103,45,180,176,163,134,139,143,94,100,105,154,154,149,174,149,112,158,116,51,156,116,47,153,118,60,207,180,146,237,214,198,207,180,166,156,131,119,174,153,145,148,131,125,125,110,107,93,85,79,86,84,79,74,74,71,59,57,53,76,68,58,137,125,112,143,133,122,133,124,114,106,98,89,86,81,74,87,85,78,84,85,78,75,76,71,50,49,43,40,30,15,95,75,44,132,103,57,183,183,175,108,116,122,142,151,158,165,169,168,177,156,122,155,112,50,159,118,51,122,89,47,213,197,179,237,224,226,220,191,188,164,135,131,183,159,155,156,137,132,125,108,104,120,111,104,78,76,69,80,80,77,45,44,40,91,85,77,175,165,154,157,147,137,155,147,138,107,100,92,87,83,77,103,102,96,88,88,79,78,79,73,59,59,59,41,36,33,59,46,31,104,81,46,188,191,189,100,108,116,135,144,153,170,175,178,187,167,136,166,120,59,173,123,55,134,93,44,117,95,80,194,182,188,199,171,164,170,142,133,185,161,151,189,171,159,134,119,106,117,107,95,102,98,89,84,84,79,38,38,34,125,121,113,210,201,192,160,152,142,146,139,130,93,89,82,83,80,75,94,93,88,104,104,94,85,87,81,73,75,78,55,53,55,62,55,48,76,56,26,189,194,194,90,96,105,127,134,144,175,180,185,174,156,133,166,123,68,178,123,53,159,109,47,97,68,44,168,154,152,168,144,126,137,114,94,186,166,148,216,202,183,160,149,129,123,113,98,120,114,105,115,114,109,50,50,47,150,147,140,194,187,178,155,149,140,123,118,111,91,88,83,84,83,79,84,84,80,95,95,85,86,87,81,84,87,89,73,73,73,79,74,64,73,55,24,189,192,193,93,95,103,152,154,163,185,188,192,119,110,98,136,106,66,173,124,58,167,116,50,103,72,39,147,132,120,145,125,103,167,149,127,189,174,155,226,216,200,180,172,157,141,131,117,126,117,107,117,114,109,71,71,68,154,152,147,186,181,174,149,144,136,114,110,104,87,85,80,80,80,76,72,73,70,80,80,72,99,100,94,100,101,99,90,88,81,97,89,69,94,73,34,194,196,196,108,107,112,168,167,172,186,186,188,105,109,109,99,89,67,156,119,62,167,122,55,100,74,34,115,106,88,138,123,103,198,185,169,190,180,169,172,165,159,145,140,140,154,143,134,146,136,125,103,100,95,71,71,70,152,152,149,179,175,170,137,133,127,130,128,122,110,109,105,85,86,83,91,93,91,95,96,90,109,110,104,115,116,111,100,96,80,97,85,53,117,95,47,197,197,197,132,129,136,172,167,174,184,178,181,130,137,142,78,83,77,140,120,88,155,125,77,115,94,52,130,120,93,143,131,116,230,221,211,242,236,230,145,138,137,135,130,130,131,121,112,121,112,101,108,104,95,95,88,75,144,134,118,168,159,146,152,147,138,112,108,101,87,85,80,71,72,68,87,88,87,105,104,99,112,109,99,120,110,93,103,86,54,121,96,48,136,104,48,203,203,204,146,146,160,168,164,178,191,182,188,168,170,172,78,86,90,126,125,126,138,126,113,138,121,82,96,80,37,154,143,133,173,163,155,162,152,141,140,132,117,113,106,88,113,106,90,101,101,92,105,101,87,112,90,58,171,143,104,156,138,109,148,141,126,135,130,118,109,105,97,78,76,72,79,79,77,94,93,94,101,91,82,107,83,55,125,88,45,151,108,55,144,104,46,214,215,215,163,166,180,164,167,184,183,184,194,176,182,186,94,102,105,96,96,102,156,149,145,148,137,111,106,93,61,129,116,105,118,105,95,114,102,89,116,105,89,102,91,73,115,110,98,86,91,88,101,103,95,144,128,102,118,96,64,68,56,32,128,120,105,133,126,115,75,69,61,60,56,51,58,56,53,71,70,65,102,93,78,116,94,64,143,112,68,150,116,64,140,110,54,212,211,205,178,184,192,167,175,189,173,181,193,176,184,188,124,131,133,86,88,96,141,139,143,153,148,141,135,128,111,104,90,80,77,64,55,134,121,108,124,111,96,129,117,100,147,143,133,85,92,93,92,96,93,150,139,120,132,117,93,117,109,92,107,99,86,75,68,58,64,59,52,44,41,39,65,62,60,86,69,40,133,105,59,155,119,62,160,120,54,154,115,45,151,111,46,199,192,180,187,189,187,171,176,181,174,179,185,177,182,184,144,149,152,86,90,99,119,121,132,122,124,130,137,136,135,144,134,126,70,59,51,129,118,108,108,97,86,145,134,123,184,176,168,116,118,118,73,75,73,131,119,103,137,124,105,134,129,118,89,86,78,51,49,44,52,51,50,47,49,52,90,90,93,121,91,60,163,118,68,171,121,64,164,113,52,158,111,50,149,107,46,165,156,146,195,193,187,179,178,175,177,173,172,181,181,180,152,157,160,99,103,111,131,135,146,171,175,185,103,105,111,93,90,87,80,77,73,93,90,86,122,118,116,178,173,173,191,182,177,150,148,148,100,100,101,89,78,66,87,77,63,60,61,57,46,52,54,38,46,51,24,33,41,46,57,69,60,71,83,108,100,75,144,125,82,144,123,76,128,109,61,127,113,69,120,105,63,117,120,124,195,200,200,177,178,176,178,169,168,181,179,179,138,144,147,83,87,91,150,153,159,245,247,250,219,222,225,133,140,144,134,141,147,149,156,164,176,182,192,190,196,208,194,192,197,168,172,181,125,133,143,110,109,109,61,62,62,35,49,58,34,54,68,49,70,87,58,81,102,61,85,110,58,84,111,69,99,122,72,101,119,78,104,120,69,96,112,59,92,112,55,90,115,79,105,133,175,197,213,174,183,192,176,172,177,177,177,182,140,146,150,109,112,113,211,211,209,253,252,247,252,253,252,208,224,232,124,143,157,114,132,149,124,141,162,116,133,156,122,133,152,104,124,148,68,93,119,68,87,104,60,82,101,52,84,111,50,84,110,51,85,115,56,93,125,56,94,131,51,91,130,43,96,135,51,104,141,59,108,142,48,97,132,43,97,137,42,95,132,41,89,135,96,137,168,144,168,188,168,174,188,178,182,192,165,170,174,165,166,164,246,245,237,253,251,241,227,231,228,110,136,153,60,88,111,53,80,105,49,76,105,49,75,107,48,72,101,45,79,115,42,81,120,46,81,113,42,82,116,38,86,125,46,90,125,46,89,126,43,87,128,42,89,132,46,93,139,46,94,137,50,96,137,55,96,135,53,94,134,51,95,139,45,90,133,29,91,141,29,87,130,59,102,134,131,153,176,166,179,191,132,136,137,194,189,181,254,250,242,241,245,245,141,159,175,61,94,127,50,84,118,50,84,119,51,85,121,49,83,120,50,84,116,47,86,117,42,84,117,39,82,115,34,79,113,35,83,120,39,86,125,38,85,125,42,89,130,45,92,134,56,103,145,62,103,142,59,101,142,56,102,146,50,99,144,46,94,140,51,103,149,48,111,162,30,94,140,34,85,124,73,106,136,128,148,167,128,136,143,215,213,209,255,253,249,187,198,205,66,93,118,54,91,128,50,88,125,52,90,127,52,90,127,46,83,121,45,82,115,43,82,113,41,81,112,36,80,113,39,83,117,40,86,123,40,89,131,43,92,134,46,95,138,59,108,150,62,110,152,64,109,147,59,108,149,54,108,154,50,105,152,70,123,167,83,137,182,52,114,165,35,99,147,31,86,130,41,83,122,66,95,126,128,145,164,224,229,234,240,245,247,124,143,153,58,92,114,49,87,123,56,94,131,54,92,129,44,82,119,44,82,119,47,83,119,46,84,119,43,83,119,43,86,123,44,88,127,44,90,131,45,97,141,54,106,150,58,110,154,54,105,150,46,97,141,43,95,140,36,91,138,51,108,158,73,130,178,85,138,182,76,125,169,50,110,162,35,98,149,29,89,138,35,86,133,44,83,126,78,106,138,202,219,233,211,228,234,97,126,140,65,104,126,54,94,129,48,87,124,58,97,133,48,87,123,40,80,116,45,82,119,47,84,122,48,87,126,47,89,130,46,89,132,51,97,140,39,92,138,39,93,139,48,102,148,47,101,147,39,93,139,28,85,133,40,101,153,67,129,182,67,126,176,46,98,142,51,96,139,50,108,161,35,97,147,32,92,143,33,88,141,41,88,138,46,84,125,104,133,159,170,197,211,64,100,119,54,97,121,52,94,128,53,95,130,61,103,139,58,100,135,54,96,131,45,83,120,42,79,118,41,80,120,46,88,130,49,92,135,46,92,136,42,95,139,40,93,138,39,92,136,37,90,135,40,93,138,44,102,151,63,125,178,47,110,164,31,90,140,15,60,103,51,93,136,68,124,177,42,100,148,31,88,137,38,91,146,37,87,139,43,89,132,42,79,113,71,107,133,49,89,114,31,77,105,27,71,105,38,82,117,49,93,128,56,100,135,58,102,137,53,92,128,56,94,131,60,99,137,57,99,139,53,97,138,50,95,137,45,94,136,39,88,131,33,83,125,42,91,133,62,112,154,79,132,179,73,131,181,56,116,168,38,97,146,13,64,108,40,85,127,61,116,168,49,102,148,35,85,132,43,91,143,39,90,139,42,92,134,44,88,125,40,81,112,42,85,115,27,72,104,23,67,102,30,74,109,27,71,106,29,73,108,36,80,115,47,86,120,56,95,128,62,101,135,66,109,144,75,119,156,69,113,152,49,95,134,43,88,127,43,88,127,60,105,144,85,130,170,109,156,197,93,145,190,60,115,164,26,82,130,29,82,126,20,64,107,54,107,160,56,105,149,45,89,132,43,86,134,40,89,134,40,92,132,40,87,123,38,81,115,36,79,114,26,69,105,22,66,101,29,73,108,25,69,104,29,73,108,19,63,98,18,58,89,32,70,100,47,87,118,61,104,137,74,119,152,66,111,145,53,96,131,52,95,130,45,87,123,67,109,145,89,131,167,105,146,182,89,135,175,48,99,145,24,77,124,34,84,129,21,67,110}
+
+
+#define IMG_DATA {235,235,235,231,231,231,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,232,233,233,233,233,233,233,233,233,233,233,233,233,233,233,233,233,232,233,233,231,233,232,231,233,231,233,233,230,233,232,232,232,234,232,231,234,232,232,232,233,233,230,232,233,231,233,233,233,232,232,232,232,232,232,232,232,232,233,233,233,233,233,233,232,232,232,238,238,238,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,236,236,236,236,236,236,236,236,236,236,236,236,236,236,236,236,236,236,237,234,233,236,234,233,236,236,234,234,236,234,234,235,237,234,234,238,235,236,237,236,236,235,236,236,234,236,236,236,235,235,235,235,235,235,235,235,235,236,236,236,236,236,236,235,235,235,237,237,237,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,235,235,235,235,234,234,236,233,231,236,234,231,235,235,234,234,235,236,227,230,233,231,235,238,231,233,235,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,235,235,235,235,235,235,234,234,234,238,238,238,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,234,235,235,235,235,235,234,233,233,230,232,232,231,228,230,232,223,226,231,186,192,197,209,216,219,207,210,213,228,228,230,236,235,235,234,234,234,234,234,234,234,234,234,234,234,234,235,235,235,235,235,235,235,235,235,237,237,237,234,234,234,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,235,234,234,234,234,234,234,235,235,235,235,235,235,234,234,234,234,234,234,235,235,235,235,235,235,236,238,236,233,237,237,219,225,230,203,210,219,163,172,179,195,205,208,214,218,221,230,229,232,237,235,237,235,235,235,235,235,235,235,235,235,235,236,236,236,236,236,236,236,236,236,236,236,239,239,238,236,235,235,236,235,235,236,235,235,236,235,235,236,235,235,235,236,235,235,235,235,234,234,234,235,235,235,237,236,236,237,236,236,234,235,236,232,233,234,235,237,237,229,231,232,208,216,218,194,205,210,185,198,207,174,188,200,165,179,189,184,196,202,207,215,220,226,228,232,236,235,237,236,236,235,236,236,235,236,236,235,236,236,236,237,237,237,237,237,237,237,237,237,228,229,229,228,227,228,232,230,231,231,228,230,234,232,233,237,236,236,237,237,235,236,237,235,237,235,236,237,235,236,239,236,237,239,237,238,225,229,230,224,228,229,233,237,238,221,226,228,183,197,204,161,180,190,159,180,191,154,176,190,144,163,177,143,159,171,156,169,177,198,206,211,233,238,239,236,237,234,235,236,233,235,235,235,235,236,236,236,238,237,237,237,237,239,237,238,212,220,222,224,230,233,230,234,238,227,232,234,229,234,234,234,237,236,237,238,235,238,237,236,239,237,238,239,237,238,239,236,237,240,238,239,201,204,203,219,222,221,233,236,235,214,218,218,193,204,210,185,201,210,184,201,211,173,191,203,165,182,196,159,174,187,162,176,185,186,199,204,229,239,240,234,239,238,233,238,237,233,238,238,234,239,238,236,239,238,237,239,238,238,238,238,216,234,241,221,236,243,225,238,246,225,239,243,227,240,240,231,238,237,236,237,235,238,236,235,238,236,237,238,236,237,237,237,237,239,239,239,197,198,196,220,221,218,233,234,231,230,231,229,209,213,217,209,216,222,219,228,235,208,218,227,209,221,234,210,224,235,217,233,240,218,235,241,225,240,243,228,238,240,228,239,240,230,240,240,230,240,239,235,240,239,237,240,239,238,238,238,118,140,149,119,138,148,124,142,153,136,155,161,172,188,191,225,234,233,235,236,233,237,234,232,236,233,234,235,235,235,235,237,236,233,237,235,214,216,214,226,228,226,232,234,232,236,237,236,228,230,232,227,230,235,231,236,241,225,232,239,225,237,247,217,233,243,201,219,226,185,204,211,172,189,195,167,179,186,167,180,185,186,199,201,223,235,235,235,241,239,236,240,239,238,240,239,109,130,141,103,121,133,108,125,137,111,127,137,146,159,165,222,229,231,227,228,225,229,226,224,236,232,233,234,234,234,231,236,234,230,237,235,229,234,235,231,235,236,232,237,238,230,235,236,231,236,238,231,237,240,229,237,241,223,232,238,191,206,213,164,184,191,146,165,172,137,156,163,134,149,159,128,140,153,121,133,143,149,162,166,216,228,229,234,241,239,235,240,238,237,240,239,195,212,224,188,202,215,199,211,224,200,211,223,209,217,227,223,227,231,213,213,211,211,209,206,216,213,214,220,222,222,219,226,225,210,221,219,209,219,223,211,221,225,216,225,230,220,229,233,225,234,237,226,236,239,225,237,241,218,231,237,183,204,208,175,198,203,181,200,207,178,194,202,186,197,211,170,178,196,142,151,164,185,195,202,219,230,233,231,240,238,234,241,239,236,240,239,193,207,222,191,202,217,202,211,224,214,217,234,223,225,241,214,219,227,203,208,208,171,174,174,177,180,183,207,213,214,174,184,188,98,112,121,93,114,126,101,121,132,111,129,139,122,138,147,137,152,161,153,167,174,202,216,220,223,236,237,218,232,235,220,233,238,223,234,240,217,226,233,221,228,237,212,219,229,196,203,212,222,230,237,219,227,234,221,230,233,232,239,242,235,241,242,113,130,152,111,125,147,113,125,141,125,131,151,138,145,165,170,182,193,191,201,205,190,199,204,208,219,226,216,230,234,158,172,183,54,71,92,45,70,91,49,73,91,53,73,90,66,84,98,102,114,129,159,168,179,221,227,233,234,239,241,233,237,241,227,231,237,223,228,233,207,211,217,202,208,212,211,218,220,212,219,223,199,206,214,179,186,196,188,197,205,211,221,227,221,231,234,61,81,108,69,86,114,63,79,100,68,85,102,123,141,155,139,155,164,151,157,164,195,200,207,214,228,234,206,223,228,163,180,190,103,121,138,95,112,131,101,117,135,138,151,168,181,192,207,207,212,223,221,222,232,219,219,227,205,203,212,183,186,195,158,166,174,147,154,163,131,138,147,125,133,140,130,139,144,136,146,152,133,142,151,128,137,147,138,153,160,182,197,203,197,212,216,40,53,77,58,70,94,85,98,116,127,144,153,132,151,156,96,107,110,119,115,118,163,158,161,173,180,182,184,194,197,182,194,198,181,193,200,183,194,202,198,209,217,218,228,236,200,210,217,174,181,186,159,165,172,145,150,159,132,136,149,116,125,138,98,111,123,94,106,118,99,111,123,105,118,128,107,121,130,122,135,145,138,151,161,150,164,174,157,174,184,188,206,213,185,203,208,13,15,35,26,29,47,134,140,151,206,216,220,138,150,150,118,123,123,141,133,134,172,162,162,181,181,180,207,209,211,220,224,225,228,234,233,224,234,232,230,241,240,226,238,238,176,189,190,144,159,163,138,154,162,142,158,170,145,163,177,154,171,187,149,165,182,149,165,182,154,171,187,157,174,189,160,177,191,173,190,204,187,204,217,190,207,218,178,196,208,165,183,193,157,175,183,5,5,24,58,62,79,200,207,217,225,232,239,197,205,212,199,207,211,212,212,218,226,224,229,229,230,237,233,236,246,232,238,245,230,238,239,209,221,220,223,238,239,221,238,241,210,228,234,198,217,228,180,200,214,193,216,230,188,213,229,189,212,231,194,214,234,192,212,232,184,204,224,172,193,212,171,191,209,161,181,197,144,165,179,136,156,169,131,146,161,128,143,158,138,154,165,39,45,71,145,155,179,190,204,222,186,196,216,184,197,217,192,211,229,194,211,230,194,208,227,194,206,227,191,203,228,192,207,228,190,207,221,177,193,207,180,198,215,154,176,193,147,169,188,145,161,184,156,171,195,146,163,186,113,133,156,114,137,161,132,157,180,126,150,173,111,135,158,92,115,138,91,112,135,93,114,133,94,116,131,105,125,140,121,133,151,129,141,158,129,142,156,122,135,161,162,179,207,143,160,194,137,154,189,131,152,187,128,152,190,127,150,192,130,150,193,131,150,192,128,147,190,127,147,189,129,149,189,129,149,188,124,145,186,104,126,163,100,122,154,102,120,154,118,134,170,112,128,163,94,109,145,94,112,148,94,117,153,87,112,144,83,103,136,80,97,130,83,103,134,93,111,139,101,117,141,108,121,144,115,125,146,121,133,148,130,144,156,73,87,109,76,90,113,77,90,122,80,93,127,84,98,134,87,102,142,87,102,147,90,105,150,94,111,152,102,119,160,107,124,165,113,131,172,115,137,181,118,136,186,118,132,180,120,133,175,115,136,172,110,133,168,106,127,163,100,119,155,95,109,148,85,101,139,79,97,132,80,92,127,80,94,129,77,100,133,80,100,129,82,98,122,92,104,126,113,119,138,125,135,146,136,149,156,13,25,41,3,11,25,9,16,35,18,26,48,18,26,52,21,25,56,20,25,58,22,30,61,26,36,62,34,43,70,42,51,77,48,59,87,52,69,106,60,75,121,66,77,126,70,79,126,71,87,127,72,88,126,67,81,120,60,72,112,55,67,106,53,68,104,53,69,103,57,69,102,57,71,105,57,78,110,72,89,115,87,100,119,104,113,128,120,124,136,130,136,141,137,146,149,36,46,55,11,16,20,8,13,19,32,44,53,36,45,58,22,25,41,8,11,30,3,8,24,1,4,17,0,2,15,0,2,15,0,4,20,6,13,42,5,18,56,1,19,60,3,23,62,13,29,71,24,38,81,21,33,77,21,31,76,21,38,78,22,44,79,30,50,83,39,58,90,57,70,101,85,90,118,113,115,138,123,123,138,116,115,125,122,123,128,134,139,137,153,160,158,35,41,45,26,27,26,13,19,18,27,41,41,71,81,84,70,70,76,49,50,57,27,31,37,15,15,21,5,5,11,2,2,7,0,0,7,17,17,35,57,64,91,31,50,78,10,36,62,4,30,60,4,30,62,7,30,63,14,35,69,25,43,74,41,55,83,62,71,99,86,97,123,122,124,146,144,131,149,132,120,135,114,105,114,117,111,116,132,134,133,146,152,146,172,179,175,16,15,17,13,10,9,4,10,8,3,12,11,45,44,46,65,52,57,54,43,47,36,33,35,18,18,20,4,4,7,2,2,4,0,1,3,7,8,15,118,117,134,161,158,179,131,128,148,112,112,131,105,105,125,105,103,124,109,105,127,118,107,126,138,115,133,154,126,144,151,126,141,127,106,116,105,86,91,106,94,97,120,116,116,129,130,129,142,147,144,164,172,165,184,194,190,40,40,35,12,10,7,0,3,3,0,4,4,12,6,7,30,12,17,32,12,17,21,10,12,7,6,7,2,1,3,2,1,2,3,2,3,0,0,2,68,58,64,182,128,146,205,130,148,196,127,144,194,123,141,195,119,137,187,113,129,172,110,122,150,96,106,123,75,83,103,66,69,95,71,70,104,93,88,122,118,113,129,132,126,132,141,135,152,162,158,171,182,176,185,197,194,69,77,64,26,29,21,1,1,1,1,1,2,4,1,0,12,2,5,18,3,9,12,2,5,4,1,2,2,0,0,2,0,0,4,0,1,1,1,1,32,12,11,153,45,59,203,47,68,195,46,67,191,48,69,179,50,67,155,49,59,119,42,49,91,38,42,81,48,46,94,77,71,117,110,102,125,126,116,125,128,120,129,135,128,144,153,147,162,176,171,173,187,183,184,198,196,83,94,82,47,52,43,1,1,1,2,1,2,2,0,0,5,1,2,7,1,5,4,0,2,1,0,0,1,0,0,1,0,0,3,0,0,1,2,0,27,3,2,142,25,38,205,32,54,198,25,46,169,25,43,121,25,36,85,29,34,74,41,39,85,66,56,102,92,82,121,113,105,128,124,115,122,126,115,121,127,118,132,139,131,147,157,150,165,179,174,176,191,187,186,201,199,92,102,93,54,60,50,6,7,3,3,2,1,2,2,0,1,3,1,1,3,3,1,2,2,1,1,1,1,0,0,1,0,0,1,1,1,0,3,2,15,1,0,102,19,28,157,31,47,117,17,23,74,13,12,56,27,22,74,58,55,99,90,81,115,115,99,122,126,111,124,124,112,123,123,113,125,130,119,128,135,126,136,145,137,148,159,151,162,176,171,177,192,188,188,202,201,87,99,89,43,51,37,19,23,11,11,12,4,8,10,2,5,11,4,2,10,4,2,7,2,3,4,1,3,4,1,3,4,1,2,3,2,0,6,6,4,5,2,42,13,13,71,21,24,53,27,25,57,50,41,80,77,62,113,98,82,132,113,101,134,126,113,123,126,112,116,125,111,120,128,115,131,138,126,139,148,137,143,154,145,156,168,161,169,184,179,182,197,193,188,202,201,82,96,82,46,57,36,36,44,22,31,35,17,27,30,15,22,28,15,17,26,13,16,23,12,18,21,12,19,21,13,20,22,14,19,23,15,19,27,20,23,31,21,37,40,27,64,55,45,87,70,67,104,88,81,116,102,85,128,112,88,139,121,105,131,122,110,117,122,107,115,127,112,123,133,119,131,139,127,139,149,138,148,160,151,159,172,164,174,189,183,185,200,196,187,202,200,85,101,83,62,75,48,58,67,38,55,61,37,51,56,35,47,53,33,46,53,34,48,55,38,49,55,40,51,56,41,53,58,44,55,62,46,59,67,45,68,71,48,81,84,59,104,96,74,116,103,83,127,109,92,133,116,97,127,121,97,127,127,107,118,124,106,114,125,108,122,131,117,129,136,123,136,145,133,141,152,141,149,162,153,158,171,163,168,183,178,180,195,191,186,200,199}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_parameter.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_parameter.h new file mode 100644 index 0000000..423d069 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_parameter.h @@ -0,0 +1,43 @@ +#define CONV1_IM_DIM 32
+#define CONV1_IM_CH 3
+#define CONV1_KER_DIM 5
+#define CONV1_PADDING 2
+#define CONV1_STRIDE 1
+#define CONV1_OUT_CH 32
+#define CONV1_OUT_DIM 32
+
+#define POOL1_KER_DIM 3
+#define POOL1_STRIDE 2
+#define POOL1_PADDING 0
+#define POOL1_OUT_DIM 16
+
+#define CONV2_IM_DIM 16
+#define CONV2_IM_CH 32
+#define CONV2_KER_DIM 5
+#define CONV2_PADDING 2
+#define CONV2_STRIDE 1
+#define CONV2_OUT_CH 16
+#define CONV2_OUT_DIM 16
+
+#define POOL2_KER_DIM 3
+#define POOL2_STRIDE 2
+#define POOL2_PADDING 0
+#define POOL2_OUT_DIM 8
+
+#define CONV3_IM_DIM 8
+#define CONV3_IM_CH 16
+#define CONV3_KER_DIM 5
+#define CONV3_PADDING 2
+#define CONV3_STRIDE 1
+#define CONV3_OUT_CH 32
+#define CONV3_OUT_DIM 8
+
+#define POOL3_KER_DIM 3
+#define POOL3_STRIDE 2
+#define POOL3_PADDING 0
+#define POOL3_OUT_DIM 4
+
+#define IP1_DIM 4*4*32
+#define IP1_IM_DIM 4
+#define IP1_IM_CH 32
+#define IP1_OUT 10
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_weights.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_weights.h new file mode 100644 index 0000000..2c3cedd --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_weights.h @@ -0,0 +1,26 @@ +#define CONV1_WT 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+
+#define CONV1_BIAS {-49,-18,-7,-20,-12,-15,7,2,-10,-84,-72,-65,-53,-6,-87,-63,-64,-28,-28,-4,-3,-10,-52,-15,-5,-7,-31,-44,-102,-19,-5,-65}
+
+#define CONV2_WT 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+
+#define CONV2_BIAS {55,50,34,43,-37,35,-21,10,35,-53,-76,7,14,-1,92,20}
+
+#define CONV3_WT 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+
+#define CONV3_BIAS {18,36,-46,-45,64,8,13,-19,28,1,14,-57,23,20,-2,32,48,-11,85,73,-7,52,125,33,125,13,92,-72,89,-1,11,70}
+
+#define IP1_WT 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34,-1,-20}
+
+#define IP1_BIAS {30,-121,-51,77,40,20,46,-35,28,-33}
+
+#define CONV1_BIAS_LSHIFT 6
+#define CONV1_OUT_RSHIFT 9
+#define CONV2_BIAS_LSHIFT 4
+#define CONV2_OUT_RSHIFT 9
+#define CONV3_BIAS_LSHIFT 1
+#define CONV3_OUT_RSHIFT 7
+#define IP1_BIAS_LSHIFT 1
+#define IP1_OUT_RSHIFT 8
+#define INPUT_MEAN_SHIFT {125,123,114}
+#define INPUT_RIGHT_SHIFT {8,8,8}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/Compiler/EventRecorderConf.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/Compiler/EventRecorderConf.h new file mode 100644 index 0000000..5958233 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/Compiler/EventRecorderConf.h @@ -0,0 +1,44 @@ +/*------------------------------------------------------------------------------
+ * MDK - Component ::Event Recorder
+ * Copyright (c) 2016 ARM Germany GmbH. All rights reserved.
+ *------------------------------------------------------------------------------
+ * Name: EventRecorderConf.h
+ * Purpose: Event Recorder Configuration
+ * Rev.: V1.0.0
+ *----------------------------------------------------------------------------*/
+
+//-------- <<< Use Configuration Wizard in Context Menu >>> --------------------
+
+// <h>Event Recorder
+
+// <o>Number of Records
+// <8=>8 <16=>16 <32=>32 <64=>64 <128=>128 <256=>256 <512=>512 <1024=>1024
+// <2048=>2048 <4096=>4096 <8192=>8192 <16384=>16384 <32768=>32768
+// <65536=>65536 <131072=>131072 <262144=>262144 <524288=>524288
+// <1048576=>1048576
+// <i>Configure size of Event Record Buffer (each record is 16 bytes)
+// <i>Must be 2^n (min=8, max=1048576)
+#define EVENT_RECORD_COUNT 64U
+
+// <o>Time Stamp Source
+// <0=> DWT Cycle Counter <1=> SysTick
+// <3=> User Timer (Normal Reset) <4=> User Timer (Power-On Reset)
+// <i>Selects source for 32-bit time stamp
+#define EVENT_TIMESTAMP_SOURCE 1
+
+// <h>SysTick Configuration
+// <i>Configure values when Time Stamp Source is set to SysTick
+
+// <o>SysTick Input Clock Frequency [Hz] <1-1000000000>
+// <i>Defines SysTick input clock (typical identical with processor clock)
+#define SYSTICK_CLOCK 100000000U
+
+// <o>SysTick Interrupt Period [us] <1-1000000000>
+// <i>Defines time period of the SysTick timer interrupt
+#define SYSTICK_PERIOD_US 1000U
+
+// </h>
+
+// </h>
+
+//------------- <<< end of configuration section >>> ---------------------------
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM0/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM0/RTE_Components.h new file mode 100644 index 0000000..73f80ad --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM0/RTE_Components.h @@ -0,0 +1,24 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru'
+ * Target: 'ARMCM0'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+#define RTE_Compiler_EventRecorder
+ #define RTE_Compiler_EventRecorder_DAP
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_EVR /* Compiler I/O: STDOUT EVR */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM3/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM3/RTE_Components.h new file mode 100644 index 0000000..1f91822 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM3/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru'
+ * Target: 'ARMCM3'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM4_FP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM4_FP/RTE_Components.h new file mode 100644 index 0000000..3df85a0 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM4_FP/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru'
+ * Target: 'ARMCM4_FP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM7_SP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM7_SP/RTE_Components.h new file mode 100644 index 0000000..04bee02 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM7_SP/RTE_Components.h @@ -0,0 +1,22 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru'
+ * Target: 'ARMCM7_SP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp new file mode 100644 index 0000000..efb3257 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp @@ -0,0 +1,221 @@ +/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project: CMSIS NN Library
+* Title: arm_nnexamples_gru.cpp
+*
+* Description: Gated Recurrent Unit Example
+*
+* Target Processor: Cortex-M4/Cortex-M7
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+* - Redistributions of source code must retain the above copyright
+* notice, this list of conditions and the following disclaimer.
+* - Redistributions in binary form must reproduce the above copyright
+* notice, this list of conditions and the following disclaimer in
+* the documentation and/or other materials provided with the
+* distribution.
+* - Neither the name of Arm LIMITED nor the names of its contributors
+* may be used to endorse or promote products derived from this
+* software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup GRUExample Gated Recurrent Unit Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates a gated recurrent unit (GRU) example with the use of fully-connected,
+ * Tanh/Sigmoid activation functions.
+ *
+ * \par Model definition:
+ * \par
+ * GRU is a type of recurrent neural network (RNN). It contains two sigmoid gates and one hidden
+ * state.
+ * \par
+ * The computation can be summarized as:
+ * <pre>z[t] = sigmoid( W_z ⋅ {h[t-1],x[t]} )
+ * r[t] = sigmoid( W_r ⋅ {h[t-1],x[t]} )
+ * n[t] = tanh( W_n ⋅ [r[t] × {h[t-1], x[t]} )
+ * h[t] = (1 - z[t]) × h[t-1] + z[t] × n[t] </pre>
+ * \image html GRU.gif "Gate Recurrent Unit Diagram"
+ *
+ * \par Variables Description:
+ * \par
+ * \li \c update_gate_weights, \c reset_gate_weights, \c hidden_state_weights are weights corresponding to update gate (W_z), reset gate (W_r), and hidden state (W_n).
+ * \li \c update_gate_bias, \c reset_gate_bias, \c hidden_state_bias are layer bias arrays
+ * \li \c test_input1, \c test_input2, \c test_history are the inputs and initial history
+ *
+ * \par
+ * The buffer is allocated as:
+ * \par
+ * | reset | input | history | update | hidden_state |
+ * \par
+ * In this way, the concatination is automatically done since (reset, input) and (input, history)
+ * are physically concatinated in memory.
+ * \par
+ * The ordering of the weight matrix should be adjusted accordingly.
+ *
+ *
+ *
+ * \par CMSIS DSP Software Library Functions Used:
+ * \par
+ * - arm_fully_connected_mat_q7_vec_q15_opt()
+ * - arm_nn_activations_direct_q15()
+ * - arm_mult_q15()
+ * - arm_offset_q15()
+ * - arm_sub_q15()
+ * - arm_copy_q15()
+ *
+ * <b> Refer </b>
+ * \link arm_nnexamples_gru.cpp \endlink
+ *
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include "arm_nnexamples_gru_test_data.h"
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#ifdef _RTE_
+#include "RTE_Components.h"
+#ifdef RTE_Compiler_EventRecorder
+#include "EventRecorder.h"
+#endif
+#endif
+
+#define DIM_HISTORY 32
+#define DIM_INPUT 32
+#define DIM_VEC 64
+
+#define USE_X4
+
+#ifndef USE_X4
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X2;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X2;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X2;
+#else
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X4;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X4;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X4;
+#endif
+
+static q7_t update_gate_bias[DIM_HISTORY] = UPDATE_GATE_BIAS;
+static q7_t reset_gate_bias[DIM_HISTORY] = RESET_GATE_BIAS;
+static q7_t hidden_state_bias[DIM_HISTORY] = HIDDEN_STATE_BIAS;
+
+static q15_t test_input1[DIM_INPUT] = INPUT_DATA1;
+static q15_t test_input2[DIM_INPUT] = INPUT_DATA2;
+static q15_t test_history[DIM_HISTORY] = HISTORY_DATA;
+
+q15_t scratch_buffer[DIM_HISTORY * 4 + DIM_INPUT];
+
+void gru_example(q15_t * scratch_input, uint16_t input_size, uint16_t history_size,
+ q7_t * weights_update, q7_t * weights_reset, q7_t * weights_hidden_state,
+ q7_t * bias_update, q7_t * bias_reset, q7_t * bias_hidden_state)
+{
+ q15_t *reset = scratch_input;
+ q15_t *input = scratch_input + history_size;
+ q15_t *history = scratch_input + history_size + input_size;
+ q15_t *update = scratch_input + 2 * history_size + input_size;
+ q15_t *hidden_state = scratch_input + 3 * history_size + input_size;
+
+ // reset gate calculation
+ // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+ arm_fully_connected_mat_q7_vec_q15(input, weights_reset, input_size + history_size, history_size, 0, 15, bias_reset,
+ reset, NULL);
+#else
+ arm_fully_connected_mat_q7_vec_q15_opt(input, weights_reset, input_size + history_size, history_size, 0, 15,
+ bias_reset, reset, NULL);
+#endif
+ // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+ arm_nn_activations_direct_q15(reset, history_size, 0, ARM_SIGMOID);
+ arm_mult_q15(history, reset, reset, history_size);
+
+ // update gate calculation
+ // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+ arm_fully_connected_mat_q7_vec_q15(input, weights_update, input_size + history_size, history_size, 0, 15,
+ bias_update, update, NULL);
+#else
+ arm_fully_connected_mat_q7_vec_q15_opt(input, weights_update, input_size + history_size, history_size, 0, 15,
+ bias_update, update, NULL);
+#endif
+
+ // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+ arm_nn_activations_direct_q15(update, history_size, 0, ARM_SIGMOID);
+
+ // hidden state calculation
+#ifndef USE_X4
+ arm_fully_connected_mat_q7_vec_q15(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+ bias_hidden_state, hidden_state, NULL);
+#else
+ arm_fully_connected_mat_q7_vec_q15_opt(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+ bias_hidden_state, hidden_state, NULL);
+#endif
+
+ // tanh function, the size of the integer bit-width should be consistent with out_shift
+ arm_nn_activations_direct_q15(hidden_state, history_size, 0, ARM_TANH);
+ arm_mult_q15(update, hidden_state, hidden_state, history_size);
+
+ // we calculate z - 1 here
+ // so final addition becomes substraction
+ arm_offset_q15(update, 0x8000, update, history_size);
+ // multiply history
+ arm_mult_q15(history, update, update, history_size);
+ // calculate history_out
+ arm_sub_q15(hidden_state, update, history, history_size);
+
+ return;
+}
+
+int main()
+{
+ #ifdef RTE_Compiler_EventRecorder
+ EventRecorderInitialize (EventRecordAll, 1); // initialize and start Event Recorder
+ #endif
+
+ printf("Start GRU execution\n");
+ int input_size = DIM_INPUT;
+ int history_size = DIM_HISTORY;
+
+ // copy over the input data
+ arm_copy_q15(test_input1, scratch_buffer + history_size, input_size);
+ arm_copy_q15(test_history, scratch_buffer + history_size + input_size, history_size);
+
+ gru_example(scratch_buffer, input_size, history_size,
+ update_gate_weights, reset_gate_weights, hidden_state_weights,
+ update_gate_bias, reset_gate_bias, hidden_state_bias);
+ printf("Complete first iteration on GRU\n");
+
+ arm_copy_q15(test_input2, scratch_buffer + history_size, input_size);
+ gru_example(scratch_buffer, input_size, history_size,
+ update_gate_weights, reset_gate_weights, hidden_state_weights,
+ update_gate_bias, reset_gate_bias, hidden_state_bias);
+ printf("Complete second iteration on GRU\n");
+
+ return 0;
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru_test_data.h b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru_test_data.h new file mode 100644 index 0000000..e0ccbe1 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru_test_data.h @@ -0,0 +1,23 @@ +#define UPDATE_GATE_WEIGHT_X2 {-62,83,-58,-89,-80,1,-93,31,101,95,121,-83,71,18,-98,-5,44,-100,-16,-73,25,62,34,-22,-16,42,9,-125,60,-78,15,-76,-76,-63,71,-25,78,-66,38,-118,-71,-120,-80,28,33,51,82,-105,26,-47,38,86,-114,44,90,-98,105,-123,24,95,-12,11,49,-35,78,104,104,17,-116,-40,-83,4,88,-110,-111,-98,18,-89,120,-84,66,-69,-8,-22,-91,-29,-41,110,55,-124,-67,103,-40,-100,1,-25,-68,-62,-89,-75,-20,-78,101,-92,-51,-97,-54,59,-78,41,34,-102,-9,-53,56,103,-55,13,81,-75,-20,-37,29,36,110,84,-80,-127,-68,-33,-70,-16,-42,-9,-104,107,-81,-16,42,-74,-63,-4,-128,-109,-105,55,-51,-68,-88,19,-39,116,-7,66,52,-29,63,-94,71,-2,-127,31,-103,120,124,41,-13,-23,127,59,-22,54,-2,32,-87,-109,85,-31,-5,-59,-122,-97,-14,-88,-19,43,-30,115,42,14,10,121,28,-63,83,-85,101,40,23,-39,74,-99,73,50,-20,123,-88,-13,-126,-25,70,-11,16,-28,121,-29,20,-69,104,117,-40,-1,-97,-12,31,32,15,-32,77,31,16,40,107,-52,-52,-89,-17,124,-95,54,48,-40,37,24,-46,42,119,5,-118,-45,-10,106,83,57,-74,-10,-56,85,37,-25,83,-31,-54,111,-78,-96,-114,-65,-100,28,-31,-111,33,66,74,-43,112,119,-80,-26,74,-81,-123,55,-126,32,-66,110,-86,-118,-21,15,16,26,13,-109,41,-16,88,81,-82,-55,-89,109,-52,118,-39,57,-16,86,-68,-10,-19,110,-50,-17,-84,103,-3,94,-8,50,15,-44,-87,6,18,8,61,66,-108,-67,-6,107,-68,25,25,25,-82,71,62,121,-31,-4,17,-6,-60,-17,116,-67,45,117,-90,12,-68,20,121,-65,-43,74,-104,-42,-69,35,4,-17,5,95,-82,18,65,43,-57,89,1,-8,37,-51,10,67,4,-50,-18,81,-120,44,98,16,-98,25,127,88,-111,-49,114,111,-17,-74,40,-18,35,19,31,48,-23,53,102,41,89,-27,87,-54,-121,-113,97,125,125,-108,58,-17,8,70,-67,-55,59,42,-85,78,27,16,66,67,54,-74,33,-19,72,77,126,-122,-7,-109,58,78,-88,15,2,16,37,-34,-114,-88,-53,88,115,19,7,-67,93,80,-48,-11,-61,31,38,29,-59,-70,0,25,106,-7,-44,53,62,19,-64,109,70,-103,-114,97,57,112,24,-66,-127,29,29,-31,-87,-125,54,-98,101,-39,56,-88,-63,-113,-73,-91,80,112,-27,-75,-42,-5,79,60,-55,23,-61,66,51,39,-91,96,-60,-64,-1,75,55,-108,73,38,75,-113,-9,-92,-92,-3,-30,93,-27,-100,-55,125,52,72,110,12,84,-83,65,-79,92,11,87,-106,35,-38,-24,-79,15,-11,-109,-22,95,82,-1,-2,-113,116,-64,93,-62,-11,101,35,-91,51,-6,21,29,25,-16,68,-103,-111,-23,-123,-80,24,-17,-7,53,23,114,-13,-105,-88,120,13,-25,-40,29,-38,-43,3,2,-121,-110,54,-43,30,66,28,60,-81,-6,-8,126,-80,64,-42,126,75,-69,-116,-41,81,94,31,-116,5,-46,5,-21,-105,78,-20,-34,54,46,-124,120,-83,44,-17,-52,-23,-110,34,-35,31,61,88,-108,-38,31,117,-26,38,-57,65,9,0,59,124,14,-39,-95,-91,107,-34,85,-83,-31,-68,-78,-86,21,-118,56,60,-3,-116,33,53,-94,85,77,91,-94,100,-89,97,88,111,36,74,-110,7,-74,91,-112,21,32,-59,98,36,10,-41,44,-114,-88,92,111,72,43,58,42,-125,-11,96,126,25,96,-105,-128,-70,85,-82,-17,103,23,-37,76,58,108,16,-116,-44,22,89,0,6,-108,27,-34,88,-125,22,-45,-116,28,-29,-70,-3,-81,-80,42,119,105,-40,-109,105,64,5,72,-32,-95,90,60,6,-29,40,-19,57,89,-50,34,-123,-32,-18,20,9,-19,-81,-45,-120,-120,20,2,18,70,75,100,64,126,-9,63,-82,114,-62,106,-11,104,-9,-13,88,-40,101,73,108,52,33,116,-54,-114,-47,85,2,-117,-80,100,-20,98,-75,-83,-24,-125,-91,-97,95,-46,15,-94,-21,53,-27,-18,65,87,112,38,-115,-27,37,-84,1,103,85,-50,36,-49,-119,68,20,119,-113,43,-67,105,44,4,-48,16,-42,83,-39,106,31,-34,-76,-51,68,82,-111,-116,-104,-118,109,-29,-6,-91,81,-102,-76,-82,64,121,27,-98,-24,-88,36,115,59,-84,-121,4,29,45,73,110,-56,-12,109,-88,85,-30,87,18,118,23,21,106,40,115,78,-72,-103,11,83,44,117,-63,98,30,115,123,-39,25,15,84,52,46,77,64,-104,125,-13,34,125,65,6,57,-128,-2,115,7,-65,-73,82,72,-109,99,43,-94,-106,-39,-4,-127,58,123,-128,29,-80,4,51,109,-50,-38,25,-13,-52,-106,87,76,44,78,101,16,-102,-20,104,23,107,-88,18,-85,119,-21,-53,-84,-7,-8,-114,23,-54,74,80,77,-40,19,-75,-41,60,77,82,-96,-121,-43,114,-124,-1,-75,32,34,-117,21,64,-87,-100,-29,-36,-45,-46,-111,4,-44,-94,-117,100,-25,-27,105,95,15,88,25,-38,-88,122,62,-62,-28,95,-86,125,-83,-9,-100,101,-124,22,21,-91,50,-100,-27,-92,115,86,85,33,-112,-43,61,114,62,-31,-84,-7,5,-26,-10,-21,-89,60,-96,48,-34,88,-80,-91,92,12,118,-2,-38,83,-50,-109,-111,-26,-109,-78,-7,84,60,-95,15,-71,112,126,71,36,39,-42,-85,-126,-68,105,-18,-127,48,-41,57,-93,13,-25,-71,66,-43,-23,122,4,-70,123,115,124,-61,-32,18,-18,49,123,-101,37,-50,-111,-73,124,-18,54,-64,93,-69,16,112,21,-56,56,127,113,-48,-57,-4,85,-84,53,44,28,-126,-59,-11,94,58,-64,112,82,127,58,50,5,-6,-102,90,-18,-86,104,17,108,-64,-22,73,-102,-17,-31,-11,-105,-40,-49,84,-82,104,57,30,112,-119,-92,78,-92,35,90,-45,-13,-75,-125,-19,-83,-75,29,15,-33,127,-14,29,-80,61,41,67,-14,-18,101,101,108,-24,-61,-90,-59,-48,-114,1,-14,106,52,109,-45,-100,74,-33,-68,-94,-68,-22,-99,31,-86,85,-27,-70,69,127,92,125,-95,117,-87,-8,-71,18,94,-90,103,-31,-1,-50,-60,-2,96,31,-1,-98,75,104,-6,-38,-24,127,94,-48,97,-96,4,-108,106,76,-31,-7,-41,58,-13,-72,-81,-116,-24,-45,46,-20,114,97,-14,125,11,22,26,27,-2,-88,-28,-76,119,50,52,66,65,120,-42,-43,-59,-56,-28,-42,-87,-18,-47,-85,74,119,97,-6,-127,-86,30,18,-43,48,-73,22,-5,34,122,9,115,-32,-63,-13,61,119,18,-113,-12,80,26,-39,-76,-101,-104,-6,48,38,-82,-52,-91,-38,112,110,115,76,69,100,-116,109,3,-35,16,94,24,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+
+#define UPDATE_GATE_WEIGHT_X4 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+
+#define RESET_GATE_WEIGHT_X2 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+
+#define RESET_GATE_WEIGHT_X4 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+
+#define HIDDEN_STATE_WEIGHT_X2 {-3,-33,59,21,117,70,0,44,108,108,-47,-80,-118,34,88,-91,-123,-108,8,51,26,82,-80,107,-100,-69,97,-90,17,19,63,111,-40,-125,110,24,58,-69,26,-31,-65,-37,-47,-41,-109,106,-100,108,-99,108,116,104,86,-50,-45,10,-53,112,34,96,-10,-39,-32,-25,69,102,-2,-4,-25,121,-1,-28,-48,-100,0,-128,60,-73,42,-32,118,-88,-113,-112,-113,70,-98,118,95,77,-52,123,-99,72,26,-102,-32,120,113,22,6,-68,84,-33,103,66,111,60,-76,33,10,25,-43,93,41,-79,110,13,67,107,-113,90,58,64,-125,79,-85,-18,76,80,-59,11,-18,-74,15,-102,99,-19,117,99,65,-50,-108,-121,-9,-104,33,94,-95,110,-48,-97,76,36,1,-58,86,-115,45,-88,38,51,123,-23,-20,-43,-37,15,91,-85,-88,6,-96,58,78,13,23,1,-43,62,-70,-108,44,30,74,90,79,-80,-20,71,-21,0,60,19,-59,-52,44,-14,77,92,-69,121,-123,-27,119,-84,79,87,24,85,118,1,-51,-96,60,102,-6,15,96,120,-109,6,35,11,-119,-109,-18,16,-112,91,-126,71,-29,121,-21,-120,37,57,-117,-39,93,56,-73,-104,77,-107,-52,111,-61,-4,44,-119,67,72,-66,36,-127,-113,-124,123,21,98,84,86,76,23,78,7,-127,-4,1,-46,-107,59,-21,53,-65,-99,-15,-98,53,-31,7,64,7,105,51,-75,50,-52,48,101,-126,-120,5,34,3,81,-39,70,41,112,25,30,79,-6,107,-11,-97,92,-84,67,49,107,60,101,-37,27,-91,-61,-96,120,-113,87,-46,68,64,102,-86,-60,13,-71,56,-105,90,-9,-35,27,103,120,39,23,-39,-1,-85,-95,-6,119,-41,-2,-69,102,102,-119,-3,-11,-125,-111,40,-115,-41,-117,-44,-7,83,123,-21,23,99,-107,43,100,-99,-3,89,3,-113,103,47,-94,-69,-38,-28,-37,49,-117,-49,-126,17,-98,37,92,55,-116,-70,-50,77,120,47,124,78,114,67,-48,6,-42,-115,85,116,-114,-46,-50,-13,70,-101,110,-55,20,-51,125,-19,-9,-15,46,30,-27,-123,114,-50,-30,-72,76,-83,71,47,-45,74,102,44,108,-26,108,-113,-43,110,-91,37,-69,76,-33,106,-76,-96,20,-117,63,-33,-5,11,-121,-51,63,-56,59,-16,-33,114,74,124,73,99,-50,51,-71,118,106,30,-92,26,-40,119,-121,2,-45,9,0,-5,-2,-89,88,-11,-85,-60,19,81,-96,75,82,-40,124,89,-36,-117,-100,-2,-34,112,101,39,-101,-106,60,59,-126,-32,96,68,-53,87,20,54,-24,46,-95,65,-112,22,60,122,-22,-106,-124,97,-37,-86,95,-110,-8,44,58,-12,-120,-45,-86,-32,-86,-94,-14,15,29,-8,-114,71,70,-93,-69,100,-123,-18,-47,-12,127,104,-102,93,-11,-73,121,87,-79,-92,46,92,-108,-107,79,121,-71,-89,16,-11,-52,72,-114,-32,-60,-9,-57,-4,10,-81,-22,68,74,76,-68,-127,96,-84,69,-3,-26,-106,-3,-87,-65,105,109,122,-103,31,-108,-86,-5,-39,85,88,67,-82,0,-25,93,61,-62,5,-54,-114,-51,-9,-114,20,49,-26,38,19,39,-103,33,-120,37,-97,32,-89,119,111,-124,-99,78,-49,-128,76,-18,-12,-109,96,90,-73,-104,59,-59,-92,123,55,54,-120,-80,-48,-16,-95,96,36,118,-119,-58,93,45,-43,-75,64,38,-2,-72,-111,22,-89,-75,-120,-42,45,108,59,-105,40,27,32,-66,121,-22,-71,-9,118,124,60,-96,47,4,14,-27,64,70,47,-91,-70,1,-44,94,-46,53,4,23,-124,-92,-95,83,-49,-81,40,-80,48,0,39,1,-113,32,40,-21,-1,-110,102,1,-74,-51,40,108,-35,-36,89,84,123,-48,-115,-115,83,-61,114,-127,-61,114,100,-82,-45,60,87,60,19,86,97,-68,40,-66,75,86,-32,-128,88,-57,-27,77,3,-27,43,-39,-62,66,5,-82,45,-104,-78,34,57,96,89,-90,66,-10,37,-110,-30,82,-58,13,94,12,115,35,117,0,80,61,-7,107,-104,-21,21,-70,-93,-94,-51,-61,39,-62,64,-82,-109,76,84,58,-47,-100,52,46,-51,88,91,8,-47,108,-80,25,-58,111,-59,-83,-75,92,98,110,54,106,65,-47,-120,-5,90,-123,101,-61,-85,-93,109,88,0,8,59,86,56,126,17,-26,58,-101,-25,35,0,-123,-3,-56,112,-128,8,17,-52,88,31,-3,105,-56,68,-1,-94,96,-19,10,-22,-88,-10,119,-44,19,42,75,-86,18,-107,89,-82,-120,76,40,84,-122,29,33,-47,17,-50,-13,23,-66,-46,85,-29,-110,42,-68,8,99,-93,-29,101,16,52,-13,127,0,86,-117,-92,-70,-32,-27,127,-123,1,34,-13,92,114,-11,29,-103,-121,-54,20,73,16,74,108,16,-61,89,50,-30,-14,116,44,-31,16,96,24,-51,7,39,-87,-69,-61,-98,61,-46,113,85,-95,103,67,99,-66,-45,-42,-70,96,104,5,-111,69,-25,99,-118,23,109,11,4,-41,-94,73,100,96,6,90,-75,-25,79,-13,-43,-6,-12,51,12,40,124,-56,81,-8,59,-60,-26,-54,33,122,85,53,-99,125,19,-26,94,41,-5,46,-48,-70,-10,41,102,-1,-98,-9,15,29,46,-66,-118,-53,45,119,-127,94,53,-58,90,124,5,-110,-98,-80,-77,77,29,19,105,-121,92,9,-124,50,-119,59,40,67,104,-12,13,103,101,47,-51,34,-66,-101,-117,112,-5,118,-48,-60,-114,38,-71,2,51,114,80,115,-5,116,20,16,-47,-19,30,24,-68,7,-30,-3,-64,-7,-34,-12,44,34,-91,-97,116,112,-99,108,-75,17,26,-14,-61,80,22,-7,34,47,-93,45,106,121,78,43,-97,39,-99,-68,-72,-7,64,-49,-82,-127,78,-64,48,18,15,126,-125,-111,-69,-111,10,-46,111,-75,123,-44,-67,-31,-96,-67,-53,-53,-106,67,-101,23,62,30,9,-114,-12,-57,-38,-78,95,-10,-3,110,88,123,-26,78,-125,114,53,10,-57,26,38,-51,73,92,-124,79,15,75,-62,109,-113,-67,1,35,52,-36,55,7,111,-43,109,101,88,122,-21,-32,-87,59,16,-122,-109,-118,17,-22,-39,53,-105,77,90,-24,-65,43,-27,113,30,-117,-30,106,37,55,59,54,-70,99,99,-73,120,97,-39,-88,-54,101,51,-76,70,-121,-68,23,-73,-31,75,-8,-63,-123,-93,96,-81,99,-95,28,-36,55,-104,32,-64,41,-97,95,-89,126,-26,-25,126,2,-26,-54,110,-86,110,74,-3,-110,56,-60,-49,117,-82,-55,-103,-112,70,-85,85,-63,82,7,75,-61,90,32,35,-115,72,73,-121,63,-84,-52,-29,-59,-4,29,64,119,127,58,-117,48,126,120,-115,-15,-10,27,27,-81,117,-5,121,-72,113,31,-13,10,27,-106,-51,81,-96,-22,19,-78,6,71,-34,123,118,75,-23,-72,-97,111,-121,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+
+#define HIDDEN_STATE_WEIGHT_X4 {-3,69,-33,102,76,60,80,19,59,-2,21,-4,-59,-59,11,-52,117,-25,70,121,-18,44,-74,-14,0,-1,44,-28,15,77,-102,92,108,-48,108,-100,99,-69,-19,121,-47,0,-80,-128,117,-123,99,-27,-118,60,34,-73,65,119,-50,-84,88,42,-91,-32,-108,79,-121,87,-123,118,-108,-88,-9,24,-104,85,8,-113,51,-112,33,118,94,1,26,-113,82,70,-95,-51,110,-96,-80,-98,107,118,-48,60,-97,102,-100,95,-69,77,76,-6,36,15,97,-52,-90,123,1,96,-58,120,17,-99,19,72,86,-109,-115,6,63,26,111,-102,45,35,-88,11,-40,-32,-125,120,38,-119,51,-109,110,113,24,22,123,-18,-23,16,58,6,-69,-68,-20,-112,-43,91,26,84,-31,-33,-37,-126,15,71,-65,103,-37,66,91,-29,-85,121,-47,111,-41,60,-88,-21,6,-120,-109,-76,106,33,-96,37,58,57,-100,10,108,25,78,-117,13,-39,-99,-43,108,93,23,93,1,56,116,41,104,-79,-43,-73,62,-104,86,110,-50,13,-70,77,-108,-107,-45,67,10,107,44,-52,30,111,-53,-113,112,90,74,-61,90,-4,34,58,96,64,79,44,-80,-119,-10,-125,-39,79,-20,67,71,72,-32,-85,-25,-18,-21,-66,0,36,-127,-96,-113,120,49,76,-117,-33,-124,-113,123,87,-49,106,-126,-76,21,-46,98,68,17,-96,-98,20,84,64,86,102,37,-117,92,63,76,-86,23,-60,55,-33,-116,-5,78,13,7,-71,-70,11,-50,-121,-127,56,-4,-105,77,-51,120,63,1,90,-46,-9,47,-56,124,59,-107,-35,59,27,78,-16,114,-33,-21,103,53,120,67,114,-48,74,-65,39,-99,23,6,124,-42,73,-15,-39,-98,-1,-115,99,85,-50,53,-85,-31,-95,116,51,-114,-71,7,-6,64,119,-46,118,-50,106,7,-41,105,-2,-13,30,70,-92,51,-69,-75,102,-101,26,110,-40,50,102,-52,-119,-55,119,20,-121,48,-3,101,-11,-51,2,125,-45,-126,-125,-120,-111,-19,9,-9,0,5,40,34,-115,-15,-5,46,-2,3,-41,81,-117,30,-89,-27,88,-39,-44,70,-7,-123,-11,114,-85,41,83,112,123,-50,-60,-30,19,25,-21,30,23,-72,81,76,-96,79,99,-6,-107,-83,75,71,82,107,43,-11,100,47,-40,-45,124,-97,-99,92,-3,74,89,102,-36,-84,89,67,3,44,-117,108,-100,49,-113,107,103,-26,-2,108,-34,60,47,101,-94,-113,112,-43,101,-37,-69,27,-38,110,39,-91,-101,-91,-28,-61,-37,37,-106,-69,60,59,121,-126,-71,-97,47,32,4,-32,-89,96,16,-89,14,119,-27,68,-11,-53,-52,111,64,-124,70,87,72,20,-114,-99,47,78,-91,54,-32,-24,-60,-49,-70,-128,1,46,-9,-95,-57,76,-44,-18,94,65,-4,-112,10,-12,-46,-109,53,22,-81,60,-22,96,4,90,23,122,68,-22,74,-73,-124,-104,-92,-106,76,-124,-68,59,-95,-59,83,97,-127,-37,96,-92,-49,123,-81,-86,-84,95,69,55,40,54,-80,-110,-3,-8,-26,-120,48,-80,0,44,-106,58,-3,-48,39,-16,1,-12,-87,-120,-65,-95,-113,96,32,-45,105,-86,109,36,40,118,-21,-32,122,-86,-103,-119,-1,-58,-110,-94,31,-14,-108,93,102,45,1,15,-86,29,-5,-43,-74,-75,-51,-8,-39,-114,85,64,40,38,108,71,88,70,67,-2,-35,-72,-36,-93,-82,-69,0,-111,89,22,84,100,-25,-123,93,-89,123,-75,-48,-18,61,-47,-62,-120,-115,-42,-115,-12,5,127,-54,45,83,108,-61,104,-114,-102,-51,59,114,-105,-127,93,-9,-11,-114,40,-61,27,114,-73,20,121,49,32,100,-66,-82,87,-26,-79,38,121,-45,-22,60,-92,19,46,39,-71,87,-9,60,92,-103,-108,33,118,19,124,86,-107,-120,79,37,60,97,-96,-68,40,-51,-66,88,-10,-61,119,89,75,91,86,8,-44,50,19,-30,-32,-47,-128,108,42,-14,75,116,88,-80,-57,25,-86,44,18,-31,-27,-58,77,111,-107,16,89,96,3,-59,-27,-83,-82,24,-120,-51,43,-75,-39,92,76,7,40,39,-62,98,66,110,84,-87,-122,-69,5,54,-82,106,29,-61,33,-98,45,65,-104,-47,-47,61,17,-46,-78,-120,34,-5,-50,113,-13,85,57,90,96,-123,23,-95,-66,103,89,101,-90,-61,-46,67,85,99,66,-85,-10,-93,-29,-66,-110,-45,37,109,-110,88,42,-42,-68,-70,-30,0,82,8,8,96,99,104,-58,59,13,86,-93,5,-29,-111,94,56,12,126,101,69,16,-25,115,17,35,-26,52,99,-13,-118,117,58,0,-101,127,23,0,109,80,-25,61,35,86,11,-117,4,-7,0,107,-123,-92,-41,-70,-94,-104,-3,-21,-56,-32,73,-27,100,21,112,-70,-128,127,96,-123,6,-93,8,-94,17,1,90,34,-75,-51,-52,-61,88,-13,-25,92,79,39,31,-62,-3,114,-13,-11,-43,64,105,-82,-56,29,-6,-103,-12,-109,68,76,-1,-121,51,-54,12,84,-94,58,96,20,40,73,124,-47,-19,-100,10,16,-56,74,81,52,-22,46,-88,108,-8,16,59,-60,-101,-26,-117,-127,-36,78,55,-54,112,33,-5,-64,7,48,111,122,118,85,-48,18,-43,15,109,53,-60,-99,-114,126,101,-125,88,125,38,19,-71,-111,122,-69,-21,-26,2,94,51,-111,-32,10,-87,41,114,-5,80,-46,59,111,16,46,115,-48,-5,-75,-122,123,-109,-70,116,-10,20,-44,-118,-67,17,41,16,102,-47,-31,-22,-96,-39,-1,-19,-98,30,-67,53,-53,-105,-9,24,15,-68,-53,77,-106,90,29,7,46,-30,67,-24,-101,-65,-66,-3,-118,-64,23,43,62,-27,-53,-7,45,-34,30,113,9,30,119,-12,-127,44,-114,-117,-12,-30,94,34,53,-91,-57,106,-38,37,-58,-97,90,116,-78,55,95,59,124,112,5,-99,-10,54,-3,-70,-110,108,-98,-75,110,99,88,99,-80,17,-77,26,123,-73,-26,120,77,-14,29,-61,78,97,-125,-39,19,80,105,22,114,-88,53,-54,-121,-7,92,34,10,101,-57,51,9,47,-124,-93,26,-76,38,70,50,45,-119,106,-51,-121,73,-68,59,121,40,78,92,23,-124,-73,67,43,104,-97,79,-31,15,75,-12,39,13,-99,75,-8,-62,-63,103,-68,101,-72,109,-123,-113,-93,47,-7,-51,64,-67,96,1,-81,34,-49,-66,-82,35,99,52,-95,28,27,-36,27,-93,-15,52,-68,55,-81,-104,117,67,-3,53,102,32,-5,-64,121,-31,-10,19,-123,41,-72,-97,113,45,-10,-111,-71,95,31,-89,-13,36,-111,39,86,126,10,-26,27,-91,48,-77,-45,-25,-106,126,-51,104,101,71,81,2,81,-26,-96,7,-114,-44,-94,-54,-22,110,19,-76,77,3,-127,-86,-78,110,6,-62,-96,121,-100,74,71,-3,-34,43,86,37,109,-110,123,56,118,-7,-33,-96,15,-60,75,-49,-23,-6,-34,57,60,117,-72,-82,-97,-104,88,30,-86,-55,111,-103,-121,-126,-63,-94,-103,-112,0,70,80,118,-46,-44,101,-85,28,85,52,91,-127,29,-88,-63,95,82,59,124,90,-86,-37,7,-116,75,72,-13,82,55,-125,-61,80,90,-75,41,-28,-41,-6,32,-62,35,88,91,61,-112,-104,-115,23,72,-102,-25,77,103,0,73,13,-121,6,81,-52,-70,59,63,113,-84,-85,-112,-9,113,-82,-52,98,-29,45,-25,59,-73,-78,-59,-96,-4,-32,-82,79,16,80,29,94,64,-7,67,-77,-5,-125,119,12,127,-9,104,-95,56,16,58,86,-117,18,66,26,-115,-55,48,59,126,-15,-90,-16,-126,15,120,75,-115,13,106,-76,80,-9,-15,-70,-10,50,-78,56,-51,110,85,-29,-102,20,15,-56,-73,-64,-23,-66,-12,-80,-128,-6,-121,-103,-6,-122,-97,40,46,-36,30,-2,-34,-2,32,116,-93,-59,-109,34,-79,126,-95,-120,-15,-40,-86,-64,71,-48,126,124,-2,-39,75,-64,127,57,30,-107,1,-74,-32,-105,-112,104,-115,80,-27,50,-86,-86,75,-13,-8,81,-13,-66,-38,-14,125,118,103,33,48,-71,10,35,-118,88,65,119,-36,-116,-107,-20,-43,-110,107,33,-27,15,-48,-119,46,-35,96,-75,88,-12,-7,90,-41,-6,-87,89,-88,-117,98,-29,-64,-41,-18,99,-83,13,61,-8,126,46,82,127,-81,-75,20,57,10,-36,81,54,-123,67,18,124,45,-55,95,50,-41,-38,-127,66,61,24,9,51,-55,113,-66,-80,28,49,-113,-71,24,113,-107,105,-71,37,47,-117,73,45,30,-74,117,114,26,6,-42,-80,-74,108,-81,110,73,88,124,-24,-43,-3,49,84,-55,-30,-125,36,102,62,-126,-45,-33,-50,98,-8,6,109,62,113,-84,-12,32,-40,24,-101,-87,-57,-56,59,-123,106,32,12,-71,-18,97,49,113,-47,105,-113,49,99,-124,-124,-81,-118,117,-12,-76,46,-9,-48,-41,-3,-54,-86,97,88,119,-82,-113,-52,-126,-93,-23,-121,127,-97,-28,-24,31,-62,-119,52,-93,75,7,33,-101,-67,67,-45,39,74,-83,127,-115,-66,4,-6,-18,-29,29,-23,68,-123,-103,31,24,117,49,8,-70,41,1,20,71,99,-40,-21,-116,-30,93,66,95,27,63,117,-49,20,-113,-92,-7,127,115,-85,97,-39,34,-67,-10,-53,123,-87,119,2,-77,68,-74,-66,22,96,-13,-12,96,-26,86,35,47,-128,-4,83,52,114,-2,-84,-14,67,-94,-53,-45,-74,-71,-94,94,-101,-122,3,-48,-14,124,-43,56,-3,58,-22,-97,-106,1,-32,57,-16,-127,51,23,-67,-59,-27,-60,125,-46,-125,-117,28,-74,29,29,-76,94,25,58,-32,-31,-101,-105,42,49,101,-115,-60,114,-55,-72,101,94,35,-14,-6,-115,-101,20,-36,-8,103,77,-42,82,61,104,85,35,-71,100,111,67,107,-90,-24,19,6,117,-87,-16,-67,-26,26,24,105,-64,102,105,-36,-7,60,109,112,-19,119,59,-81,10,-91,-9,69,124,-54,124,52,80,13,-105,21,-116,-114,-33,5,0,10,126,-93,22,-26,-115,115,94,22,105,-111,30,-7,102,44,63,0,85,-38,91,-30,98,-21,43,4,-63,-34,-124,-67,38,33,-2,45,-32,-86,12,-92,-38,29,39,121,-119,-42,-23,-30,-106,3,-12,-54,-108,7}
+
+#define UPDATE_GATE_BIAS {-85,78,113,70,33,38,8,114,70,-35,-67,65,31,-24,-70,-124,-89,104,124,-122,111,61,-87,75,-61,-98,83,-69,-63,45,-11,103}
+
+#define RESET_GATE_BIAS {-77,67,-93,-3,98,59,-121,33,49,50,41,91,-115,-33,71,47,-70,45,89,-115,72,106,-22,100,97,-100,-95,108,-33,3,14,30}
+
+#define HIDDEN_STATE_BIAS {-85,78,113,70,33,38,8,114,70,-35,-67,65,31,-24,-70,-124,-89,104,124,-122,111,61,-87,75,-61,-98,83,-69,-63,45,-11,103}
+
+#define INPUT_DATA1 {-367,-338,0,-89,453,-413,-343,-16,42,418,201,274,-352,477,-290,-92,266,-49,342,453,-398,247,-153,328,217,342,85,69,-38,351,73,128}
+
+#define INPUT_DATA2 {280,41,-322,61,315,350,504,-227,-221,-483,352,252,455,-236,344,364,-378,229,-187,-498,295,357,-511,58,-349,-458,-420,-66,-400,-379,477,-60}
+
+#define HISTORY_DATA {-38,53,105,-79,-463,51,-343,-226,-435,-282,218,441,-299,-215,-109,335,340,-471,-109,273,33,-245,-469,170,-26,-59,192,-119,76,-6,236,-145}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h new file mode 100644 index 0000000..9357424 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h @@ -0,0 +1,59 @@ +/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_tables.h
+ * Description: Extern declaration for NN tables
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _ARM_NN_TABLES_H
+#define _ARM_NN_TABLES_H
+
+#include "arm_math.h"
+
+/**
+* @brief tables for various activation functions
+*
+*/
+
+extern const q15_t sigmoidTable_q15[256];
+extern const q7_t sigmoidTable_q7[256];
+
+extern const q7_t tanhTable_q7[256];
+extern const q15_t tanhTable_q15[256];
+
+ /**
+ * @brief 2-way tables for various activation functions
+ *
+ * 2-way table, H table for value larger than 1/4
+ * L table for value smaller than 1/4, H table for remaining
+ * We have this only for the q15_t version. It does not make
+ * sense to have it for q7_t type
+ */
+extern const q15_t sigmoidHTable_q15[192];
+extern const q15_t sigmoidLTable_q15[128];
+
+extern const q15_t sigmoidLTable_q15[128];
+extern const q15_t sigmoidHTable_q15[192];
+
+#endif /* ARM_NN_TABLES_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h new file mode 100644 index 0000000..96c59c2 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h @@ -0,0 +1,1010 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nnfunctions.h
+ * Description: Public header file for CMSIS NN Library
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+/**
+ \mainpage CMSIS NN Software Library
+ *
+ * Introduction
+ * ------------
+ *
+ * This user manual describes the CMSIS NN software library,
+ * a collection of efficient neural network kernels developed to maximize the
+ * performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
+ *
+ * The library is divided into a number of functions each covering a specific category:
+ * - Neural Network Convolution Functions
+ * - Neural Network Activation Functions
+ * - Fully-connected Layer Functions
+ * - Neural Network Pooling Functions
+ * - Softmax Functions
+ * - Neural Network Support Functions
+ *
+ * The library has separate functions for operating on different weight and activation data
+ * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
+ * kernels are included in the function description. The implementation details are also
+ * described in this paper [1].
+ *
+ * Block Diagram
+ * --------
+ * \image html CMSIS-NN-OVERVIEW.PNG
+ *
+ * Examples
+ * --------
+ *
+ * The library ships with a number of examples which demonstrate how to use the library functions.
+ *
+ * Pre-processor Macros
+ * ------------
+ *
+ * Each library project have differant pre-processor macros.
+ *
+ * - ARM_MATH_DSP:
+ *
+ * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
+ *
+ * - ARM_MATH_BIG_ENDIAN:
+ *
+ * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
+ *
+ * - ARM_NN_TRUNCATE:
+ *
+ * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
+ *
+ * Copyright Notice
+ * ------------
+ *
+ * Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+ *
+ * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
+ */
+
+/**
+ * @defgroup groupNN Neural Network Functions
+ * These functions perform basic operations for neural network layers.
+ */
+
+#ifndef _ARM_NNFUNCTIONS_H
+#define _ARM_NNFUNCTIONS_H
+
+#include "arm_nnsupportfunctions.h"
+#include "arm_nn_tables.h"
+
+#define USE_INTRINSIC
+
+//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @defgroup NNConv Neural Network Convolution Functions
+ *
+ * Perform convolution layer
+ *
+ * The convolution is implemented in 2 steps: im2col and GEMM
+ *
+ * im2col is a process of converting each patch of image data into
+ * a column. After im2col, the convolution is computed as matrix-matrix
+ * multiplication.
+ *
+ * To reduce the memory footprint, the im2col is performed partially.
+ * Each iteration, only a few column (i.e., patches) are generated and
+ * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
+ *
+ */
+
+ /**
+ * @brief Basic Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Basic Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ */
+
+ arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Basic Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+ * and dim_kernel_y=1). It can be used for
+ * second half of MobileNets after depthwise separable convolution.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+ arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 version of convolution for RGB image
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This kernel is written exclusively for convolution with ch_im_in
+ * equals 3. This applies on the first layer of CNNs which has input
+ * image with RGB format.
+ */
+
+ arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q15 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in is multiple of 2
+ *
+ * ch_im_out is multipe of 2
+ *
+ */
+
+ arm_status
+ arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 depthwise separable convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 depthwise separable convolution function (non-square shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding sizes x
+ * @param[in] padding_y padding sizes y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+ arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+
+/**
+ * @defgroup FC Fully-connected Layer Functions
+ *
+ * Perform fully-connected layer
+ *
+ * Fully-connected layer is basically a matrix-vector multiplication
+ * with bias. The matrix is the weights and the input/output vectors
+ * are the activation values. Supported {weight, activation} precisions
+ * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
+ *
+ * Here we have two types of kernel functions. The basic function
+ * implements the function using regular GEMV approach. The opt functions
+ * operates with weights in interleaved formats.
+ *
+ */
+
+ /**
+ * @brief Q7 basic fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q7(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q7_opt(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q15 basic fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q15(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q15 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q15_opt(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Mixed Q15-Q7 fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Mixed Q15-Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+/**
+ * @brief Matrix-Multiplication Kernels for Convolution
+ *
+ * These functions are used within convolution layer functions for
+ * matrix multiplication.
+ *
+ * The implementation is similar to CMSIS-DSP arm_mat_mult functions
+ * with one Q7 and one Q15 operands. The Q15 operand is the im2col
+ * output which is always with 2 columns.
+ *
+ */
+
+ /**
+ * @brief Matrix-multiplication function for convolution
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ */
+
+ q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut);
+
+ /**
+ * @brief Matrix-multiplication function for convolution with reordered columns
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ */
+
+ q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut);
+
+#ifdef __cplusplus
+}
+#endif
+
+/*
+ * Other functions
+ * These layers are typically not timing critical
+ * Basic implementation is supported here
+ */
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @defgroup Acti Neural Network Activation Functions
+ *
+ * Perform activation layers, including ReLU (Rectified Linear Unit),
+ * sigmoid and tanh
+ *
+ */
+
+ /**
+ * @brief Q7 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ */
+
+ void arm_relu_q7(q7_t * data, uint16_t size);
+
+ /**
+ * @brief Q15 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ */
+
+ void arm_relu_q15(q15_t * data, uint16_t size);
+
+ /**
+ * @brief Q7 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ */
+
+ void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
+ arm_nn_activation_type type);
+
+ /**
+ * @brief Q15 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ */
+
+ void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
+ arm_nn_activation_type type);
+
+/**
+ * @defgroup Pooling Neural Network Pooling Functions
+ *
+ * Perform pooling functions, including max pooling and average pooling
+ *
+ */
+
+ /**
+ * @brief Q7 max pooling function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ */
+
+ void arm_maxpool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const uint16_t dim_im_out,
+ q7_t * bufferA,
+ q7_t * Im_out);
+
+ /**
+ * @brief Q7 average pooling function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ */
+
+ void arm_avepool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const uint16_t dim_im_out,
+ q7_t * bufferA,
+ q7_t * Im_out);
+
+/**
+ * @defgroup Softmax Softmax Functions
+ *
+ * EXP(2) based softmax function
+ *
+ */
+
+ /**
+ * @brief Q7 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ */
+
+ void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
+
+ /**
+ * @brief Q15 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ */
+
+ void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h new file mode 100644 index 0000000..05a239d --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h @@ -0,0 +1,202 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nnsupportfunctions.h
+ * Description: Public header file of support functions for CMSIS NN Library
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#ifndef _ARM_NNSUPPORTFUNCTIONS_H_
+#define _ARM_NNSUPPORTFUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+//#include <cstring>
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @brief Union for SIMD access of Q31/Q15/Q7 types
+ */
+union arm_nnword
+{
+ q31_t word;
+ /**< Q31 type */
+ q15_t half_words[2];
+ /**< Q15 type */
+ q7_t bytes[4];
+ /**< Q7 type */
+};
+
+/**
+ * @brief Struct for specifying activation function types
+ *
+ */
+typedef enum
+{
+ ARM_SIGMOID = 0,
+ /**< Sigmoid activation function */
+ ARM_TANH = 1,
+ /**< Tanh activation function */
+} arm_nn_activation_type;
+
+/**
+ * @defgroup nndata_convert Neural Network Data Conversion Functions
+ *
+ * Perform data type conversion in-between neural network operations
+ *
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ */
+
+void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+/**
+ * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ */
+
+void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad(void *source, q31_t * out1, q31_t * out2)
+{
+ q31_t inA = *__SIMD32(source)++;
+ q31_t inAbuf1 = __SXTB16(__ROR(inA, 8));
+ q31_t inAbuf2 = __SXTB16(inA);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ *out2 = __PKHTB(inAbuf1, inAbuf2, 16);
+ *out1 = __PKHBT(inAbuf2, inAbuf1, 16);
+#else
+ *out1 = __PKHTB(inAbuf1, inAbuf2, 16);
+ *out2 = __PKHBT(inAbuf2, inAbuf1, 16);
+#endif
+
+ return source;
+}
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words with reordering
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q31_t * out2)
+{
+ q31_t inA = *__SIMD32(source)++;
+#ifndef ARM_MATH_BIG_ENDIAN
+ *out2 = __SXTB16(__ROR(inA, 8));
+ *out1 = __SXTB16(inA);
+#else
+ *out1 = __SXTB16(__ROR(inA, 8));
+ *out2 = __SXTB16(inA);
+#endif
+
+ return source;
+}
+#endif
+
+/**
+ * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation
+ *
+ * Basic Math Functions for Neural Network Computation
+ *
+ */
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+ q15_t * pSrcA,
+ q15_t * pSrcB,
+ q15_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize);
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+ q7_t * pSrcA,
+ q7_t * pSrcB,
+ q7_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize);
+
+/**
+ * @brief defition to adding rouding offset
+ */
+#ifndef ARM_NN_TRUNCATE
+ #define NN_ROUND(out_shift) ( 0x1 << (out_shift - 1) )
+#else
+ #define NN_ROUND(out_shift) 0
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM0/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM0/RTE_Components.h new file mode 100644 index 0000000..bf1d6d1 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM0/RTE_Components.h @@ -0,0 +1,20 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10'
+ * Target: 'ARMCM0'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM3/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM3/RTE_Components.h new file mode 100644 index 0000000..6993e2a --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM3/RTE_Components.h @@ -0,0 +1,26 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test'
+ * Target: 'ARMCM3'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDERR /* Compiler I/O: STDERR */
+ #define RTE_Compiler_IO_STDERR_ITM /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY /* Compiler I/O: TTY */
+ #define RTE_Compiler_IO_TTY_ITM /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM4_FP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM4_FP/RTE_Components.h new file mode 100644 index 0000000..374579e --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM4_FP/RTE_Components.h @@ -0,0 +1,26 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test'
+ * Target: 'ARMCM4_FP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDERR /* Compiler I/O: STDERR */
+ #define RTE_Compiler_IO_STDERR_ITM /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY /* Compiler I/O: TTY */
+ #define RTE_Compiler_IO_TTY_ITM /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM7_SP/RTE_Components.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM7_SP/RTE_Components.h new file mode 100644 index 0000000..40dfc78 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM7_SP/RTE_Components.h @@ -0,0 +1,26 @@ +
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ * *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test'
+ * Target: 'ARMCM7_SP'
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File:
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDERR /* Compiler I/O: STDERR */
+ #define RTE_Compiler_IO_STDERR_ITM /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT /* Compiler I/O: STDOUT */
+ #define RTE_Compiler_IO_STDOUT_ITM /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY /* Compiler I/O: TTY */
+ #define RTE_Compiler_IO_TTY_ITM /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref.c new file mode 100644 index 0000000..4aa6077 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref.c @@ -0,0 +1,71 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q15_ref(const q15_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q15_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q15_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q15_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ )
+{
+ int i, j, k, l, m, n;
+ int conv_out;
+ int in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ conv_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ conv_out = bias[i] << bias_shift;
+#endif
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref_nonsquare.c new file mode 100644 index 0000000..69efcb2 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref_nonsquare.c @@ -0,0 +1,83 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void
+arm_convolve_HWC_q15_nonsquare_ref(const q15_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+
+{
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ conv_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ conv_out = bias[i] << bias_shift;
+#endif
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+ }
+ }
+ }
+}
+
+
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref.c new file mode 100644 index 0000000..aaf25ba --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref.c @@ -0,0 +1,72 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q7_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ )
+{
+ int i, j, k, l, m, n;
+ int conv_out;
+ int in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ conv_out = ((q31_t) (bias[i]) << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ conv_out = bias[i] << bias_shift;
+#endif
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ // if-for implementation
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref_nonsquare.c new file mode 100644 index 0000000..e731d07 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref_nonsquare.c @@ -0,0 +1,78 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in_x, // input image dimention x
+ const uint16_t dim_im_in_y, // input image dimention y
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel_x, // filter kernel size x
+ const uint16_t dim_kernel_y, // filter kernel size y
+ const uint16_t padding_x, // padding sizes x
+ const uint16_t padding_y, // padding sizes y
+ const uint16_t stride_x, // stride x
+ const uint16_t stride_y, // stride y
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out, // output image
+ const uint16_t dim_im_out_x, // output image dimension x
+ const uint16_t dim_im_out_y, // output image dimension y
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ )
+{
+ int i, j, k, l, m, n;
+ int conv_out;
+ int in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ conv_out = ((q31_t) (bias[i]) << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ conv_out = bias[i] << bias_shift;
+#endif
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ // if-for implementation
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in +
+ l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref.c new file mode 100644 index 0000000..970effc --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref.c @@ -0,0 +1,70 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_depthwise_separable_conv_HWC_q7_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ q7_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ )
+{
+ int i_out_y, i_out_x, i_ch_out;
+ int i_ker_y, i_ker_x;
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+ {
+ // for each output
+#ifndef ARM_NN_TRUNCATE
+ int conv_out = (bias[i_ch_out] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int conv_out = bias[i_ch_out] << bias_shift;
+#endif
+ for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++)
+ {
+ int in_row = stride * i_out_y + i_ker_y - padding;
+ int in_col = stride * i_out_x + i_ker_x - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + i_ch_out] *
+ wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out];
+ }
+ }
+ }
+ Im_out[(i_out_y * dim_im_out + i_out_x) * ch_im_out + i_ch_out] =
+ (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref_nonsquare.c new file mode 100644 index 0000000..8fa4147 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref_nonsquare.c @@ -0,0 +1,75 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in_x, // input image dimention x
+ const uint16_t dim_im_in_y, // input image dimention y
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel_x, // filter kernel size x
+ const uint16_t dim_kernel_y, // filter kernel size y
+ const uint16_t padding_x, // padding sizes x
+ const uint16_t padding_y, // padding sizes y
+ const uint16_t stride_x, // stride x
+ const uint16_t stride_y, // stride y
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ q7_t * Im_out, // output image
+ const uint16_t dim_im_out_x, // output image dimension x
+ const uint16_t dim_im_out_y, // output image dimension y
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ )
+{
+ int i_out_y, i_out_x, i_ch_out;
+ int i_ker_y, i_ker_x;
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+ {
+ // for each output
+#ifndef ARM_NN_TRUNCATE
+ int conv_out = (bias[i_ch_out] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int conv_out = bias[i_ch_out] << bias_shift;
+#endif
+ for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
+ {
+ for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
+ {
+ int in_row = stride_y * i_out_y + i_ker_y - padding_y;
+ int in_col = stride_x * i_out_x + i_ker_x - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *
+ wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
+ }
+ }
+ }
+ Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
+ (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_opt_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_opt_ref.c new file mode 100644 index 0000000..6fe5e2b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_opt_ref.c @@ -0,0 +1,120 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_mat_q7_vec_q15_opt_ref(const q15_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q7_t *pB = pM;
+ const q15_t *pA;
+ q15_t *pO = pOut;
+ const q7_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ q31_t sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ q31_t sum = *pBias++ << bias_shift;
+ q31_t sum2 = *pBias++ << bias_shift;
+ q31_t sum3 = *pBias++ << bias_shift;
+ q31_t sum4 = *pBias++ << bias_shift;
+#endif
+
+ uint16_t colCnt = dim_vec >> 1;
+
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inA2 = *pA++;
+
+ q7_t inB1 = *pB++;
+ q7_t inB3 = *pB++;
+ q7_t inB2 = *pB++;
+ q7_t inB4 = *pB++;
+
+ sum += inA1 * inB1 + inA2 * inB2;
+ sum2 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA1 * inB1 + inA2 * inB2;
+ sum4 += inA1 * inB3 + inA2 * inB4;
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+ q15_t inA = *pA++;
+ q7_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+
+ colCnt--;
+ }
+ *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+ rowCnt--;
+ }
+
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = *pBias++ << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ q15_t inA = *pA++;
+ q7_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+ rowCnt--;
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_ref.c new file mode 100644 index 0000000..6a4d385 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_ref.c @@ -0,0 +1,43 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_mat_q7_vec_q15_ref(const q15_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+ for (int i = 0; i < num_of_rows; i++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = bias[i] << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_opt_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_opt_ref.c new file mode 100644 index 0000000..475c757 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_opt_ref.c @@ -0,0 +1,119 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q15_opt_ref(const q15_t * pV, // pointer to vector
+ const q15_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q15_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q15_t *pB = pM;
+ const q15_t *pA;
+ q15_t *pO = pOut;
+ const q15_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ q31_t sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ q31_t sum = *pBias++ << bias_shift;
+ q31_t sum2 = *pBias++ << bias_shift;
+ q31_t sum3 = *pBias++ << bias_shift;
+ q31_t sum4 = *pBias++ << bias_shift;
+#endif
+
+ uint16_t colCnt = dim_vec >> 1;
+
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inA2 = *pA++;
+
+ q15_t inB1 = *pB++;
+ q15_t inB2 = *pB++;
+ sum += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum2 += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum3 += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum4 += inA1 * inB1 + inA2 * inB2;
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+ q15_t inA = *pA++;
+ q15_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+ colCnt--;
+ }
+ *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+ rowCnt--;
+ }
+
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = *pBias++ << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ q15_t inA = *pA++;
+ q15_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+ rowCnt--;
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_ref.c new file mode 100644 index 0000000..3fd0bc0 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_ref.c @@ -0,0 +1,43 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q15_ref(const q15_t * pV, // pointer to vector
+ const q15_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q15_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+ for (int i = 0; i < num_of_rows; i++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = bias[i] << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_opt_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_opt_ref.c new file mode 100644 index 0000000..cee8faa --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_opt_ref.c @@ -0,0 +1,138 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q7_opt_ref(const q7_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q7_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q7_t *pB = pM;
+ const q7_t *pA;
+ q7_t *pO = pOut;
+ const q7_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ q31_t sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+ q31_t sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ q31_t sum = *pBias++ << bias_shift;
+ q31_t sum2 = *pBias++ << bias_shift;
+ q31_t sum3 = *pBias++ << bias_shift;
+ q31_t sum4 = *pBias++ << bias_shift;
+#endif
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q7_t inA3 = *pA++;
+ q7_t inA2 = *pA++;
+ q7_t inA4 = *pA++;
+
+ q7_t inB1 = *pB++;
+ q7_t inB3 = *pB++;
+ q7_t inB2 = *pB++;
+ q7_t inB4 = *pB++;
+
+ sum += inA1 * inB1 + inA2 * inB2;
+ sum2 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA1 * inB1 + inA2 * inB2;
+ sum4 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum += inA3 * inB1 + inA4 * inB2;
+ sum2 += inA3 * inB3 + inA4 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA3 * inB1 + inA4 * inB2;
+ sum4 += inA3 * inB3 + inA4 * inB4;
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q7_t inA = *pA++;
+ q7_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+
+ colCnt--;
+ }
+ *pO++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ rowCnt--;
+ }
+
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ pA = pV;
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = *pBias++ << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ q7_t inA = *pA++;
+ q7_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8);
+
+ rowCnt--;
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_ref.c new file mode 100644 index 0000000..78c891c --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_ref.c @@ -0,0 +1,43 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q7_ref(const q7_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q7_t * pOut, // output operand
+ q15_t * vec_buffer)
+{
+ for (int i = 0; i < num_of_rows; i++)
+ {
+#ifndef ARM_NN_TRUNCATE
+ int ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+ int ip_out = bias[i] << bias_shift;
+#endif
+ for (int j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8);
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_nn_mult_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_nn_mult_ref.c new file mode 100644 index 0000000..e78850f --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_nn_mult_ref.c @@ -0,0 +1,58 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+void arm_nn_mult_q7_ref(q7_t * pSrcA,
+ q7_t * pSrcB,
+ q7_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize) {
+ uint16_t i;
+
+for (i = 0; i < blockSize; i++)
+ {
+ q31_t product = pSrcA[i] * pSrcB[i];
+#ifndef ARM_NN_TRUNCATE
+ pDst[i] = (q7_t)__SSAT((product + (0x1 << (out_shift - 1)))>>out_shift, 8);
+#else
+ pDst[i] = (q7_t)__SSAT(product >> out_shift, 8);
+#endif
+ }
+}
+
+void arm_nn_mult_q15_ref(q15_t * pSrcA,
+ q15_t * pSrcB,
+ q15_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize) {
+ uint16_t i;
+
+for (i = 0; i < blockSize; i++)
+ {
+ q31_t product = pSrcA[i] * pSrcB[i];
+#ifndef ARM_NN_TRUNCATE
+ pDst[i] = (q15_t)__SSAT((product + (0x1 << (out_shift - 1)))>>out_shift, 16);
+#else
+ pDst[i] = (q15_t)__SSAT(product >> out_shift, 16);
+#endif
+
+
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_pool_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_pool_ref.c new file mode 100644 index 0000000..b75a0a2 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_pool_ref.c @@ -0,0 +1,96 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_avepool_q7_HWC_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimension
+ const uint16_t ch_im_in, // number of input image channels
+ const uint16_t dim_kernel, // window kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const uint16_t dim_im_out, // output image dimension
+ q7_t * bufferA, // a buffer for local storage
+ q7_t * Im_out)
+{
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int sum = 0;
+ int count = 0;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ count++;
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+ }
+ }
+ }
+}
+
+void arm_maxpool_q7_HWC_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimension
+ const uint16_t ch_im_in, // number of input image channels
+ const uint16_t dim_kernel, // window kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const uint16_t dim_im_out, // output image dimension
+ q7_t * bufferA, // a buffer for local storage
+ q7_t * Im_out)
+{
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int max = -129;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+ {
+ max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ }
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+ }
+ }
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_relu_ref.c b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_relu_ref.c new file mode 100644 index 0000000..9397ef1 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_relu_ref.c @@ -0,0 +1,42 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+void arm_relu_q7_ref(q7_t * data, uint16_t size)
+{
+ uint16_t i;
+
+ for (i = 0; i < size; i++)
+ {
+ if (data[i] < 0)
+ data[i] = 0;
+ }
+}
+
+void arm_relu_q15_ref(q15_t * data, uint16_t size)
+{
+ uint16_t i;
+
+ for (i = 0; i < size; i++)
+ {
+ if (data[i] < 0)
+ data[i] = 0;
+ }
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/fully_connected_testing_weights.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/fully_connected_testing_weights.h new file mode 100644 index 0000000..31cdcb0 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/fully_connected_testing_weights.h @@ -0,0 +1,7 @@ +#define IP2_WEIGHT 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+
+#define IP4_WEIGHT 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+
+#define IP4_q7_q15_WEIGHT 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+
+#define IP4_WEIGHT_Q15 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diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/ref_functions.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/ref_functions.h new file mode 100644 index 0000000..5a25ffa --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/ref_functions.h @@ -0,0 +1,250 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _REF_FUNCTIONS_H_
+#define _REF_FUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+//#include "arm_nnsupportfunctions.h"
+#include "fully_connected_testing_weights.h"
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/*
+ *
+ * Convolution reference implemenation
+ *
+ */
+
+ void arm_convolve_HWC_q7_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ );
+
+ void arm_convolve_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in_x, // input image dimention x
+ const uint16_t dim_im_in_y, // input image dimention y
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel_x, // filter kernel size x
+ const uint16_t dim_kernel_y, // filter kernel size y
+ const uint16_t padding_x, // padding sizes x
+ const uint16_t padding_y, // padding sizes y
+ const uint16_t stride_x, // stride x
+ const uint16_t stride_y, // stride y
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out, // output image
+ const uint16_t dim_im_out_x, // output image dimension x
+ const uint16_t dim_im_out_y, // output image dimension y
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ );
+
+ void arm_convolve_HWC_q15_ref(const q15_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q15_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q15_t * bias, // bias
+ const uint16_t bias_shift, const uint16_t out_shift, q15_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ );
+ void arm_convolve_HWC_q15_nonsquare_ref(const q15_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ void arm_depthwise_separable_conv_HWC_q7_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimention
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel, // filter kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ q7_t * Im_out, // output image
+ const uint16_t dim_im_out, // output image dimension
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ );
+ void arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in_x, // input image dimention x
+ const uint16_t dim_im_in_y, // input image dimention y
+ const uint16_t ch_im_in, // number of input image channels
+ const q7_t * wt, // kernel weights
+ const uint16_t ch_im_out, // number of filters, i.e., output image channels
+ const uint16_t dim_kernel_x, // filter kernel size x
+ const uint16_t dim_kernel_y, // filter kernel size y
+ const uint16_t padding_x, // padding sizes x
+ const uint16_t padding_y, // padding sizes y
+ const uint16_t stride_x, // stride x
+ const uint16_t stride_y, // stride y
+ const q7_t * bias, // bias
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ q7_t * Im_out, // output image
+ const uint16_t dim_im_out_x, // output image dimension x
+ const uint16_t dim_im_out_y, // output image dimension y
+ q15_t * bufferA, //buffer space for input
+ q7_t * bufferB //buffer space for output
+ );
+
+/*
+ *
+ * Fully-connected reference implemenation
+ *
+ */
+
+ void arm_fully_connected_q7_ref(const q7_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q7_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+ void arm_fully_connected_q15_ref(const q15_t * pV, // pointer to vector
+ const q15_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q15_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+ void arm_fully_connected_mat_q7_vec_q15_ref(const q15_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+ void arm_fully_connected_q7_opt_ref(const q7_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q7_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+ void arm_fully_connected_q15_opt_ref(const q15_t * pV, // pointer to vector
+ const q15_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q15_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+ void arm_fully_connected_mat_q7_vec_q15_opt_ref(const q15_t * pV, // pointer to vector
+ const q7_t * pM, // pointer to matrix
+ const uint16_t dim_vec, // length of the vector
+ const uint16_t num_of_rows, // numCol of A
+ const uint16_t bias_shift, // amount of left-shift for bias
+ const uint16_t out_shift, // amount of right-shift for output
+ const q7_t * bias, q15_t * pOut, // output operand
+ q15_t * vec_buffer);
+
+/*
+ *
+ * Pooling reference implemenation
+ *
+ */
+
+ void arm_avepool_q7_HWC_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimension
+ const uint16_t ch_im_in, // number of input image channels
+ const uint16_t dim_kernel, // window kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const uint16_t dim_im_out, // output image dimension
+ q7_t * bufferA, // a buffer for local storage
+ q7_t * Im_out);
+
+ void arm_maxpool_q7_HWC_ref(const q7_t * Im_in, // input image
+ const uint16_t dim_im_in, // input image dimension
+ const uint16_t ch_im_in, // number of input image channels
+ const uint16_t dim_kernel, // window kernel size
+ const uint16_t padding, // padding sizes
+ const uint16_t stride, // stride
+ const uint16_t dim_im_out, // output image dimension
+ q7_t * bufferA, // a buffer for local storage
+ q7_t * Im_out);
+
+/*
+ *
+ * Other reference implemenation
+ *
+ */
+
+ void arm_relu_q7_ref(q7_t * data, uint16_t size);
+
+ void arm_relu_q15_ref(q15_t * data, uint16_t size);
+
+ void arm_nn_mult_q7_ref(q7_t * pSrcA, q7_t * pSrcB, q7_t * pDst, const uint16_t out_shift, uint32_t blockSize);
+
+ void arm_nn_mult_q15_ref(q15_t * pSrcA, q15_t * pSrcB, q15_t * pDst, const uint16_t out_shift, uint32_t blockSize);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.cpp b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.cpp new file mode 100644 index 0000000..41088fe --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.cpp @@ -0,0 +1,801 @@ +/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project: CMSIS NN Library
+* Title: arm_nnexamples_nn_test.cpp
+*
+* Description: Example code for NN kernel testing.
+*
+* Target Processor: Cortex-M cores
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+* - Redistributions of source code must retain the above copyright
+* notice, this list of conditions and the following disclaimer.
+* - Redistributions in binary form must reproduce the above copyright
+* notice, this list of conditions and the following disclaimer in
+* the documentation and/or other materials provided with the
+* distribution.
+* - Neither the name of ARM LIMITED nor the names of its contributors
+* may be used to endorse or promote products derived from this
+* software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+#include "arm_nnexamples_nn_test.h"
+
+//#define TEST_SIGMOID
+//#define TEST_TANH
+#define TEST_POOL
+#define TEST_RELU
+#define TEST_IP
+#define TEST_CONV
+#define TEST_NONSQUARE
+#define TEST_NNMULT
+
+int test_index = 0;
+q7_t test_flags[50];
+bool test_pass;
+
+int main()
+{
+ printf("start tests\n");
+
+ srand(1);
+
+ // common pointers for testing data
+ q7_t *test1;
+ q15_t *test2;
+ q7_t *test3;
+ q15_t *test4;
+
+ for (test_index = 0; test_index<50; test_index++) {
+ test_flags[test_index] = -1;
+ }
+ test_index = 0;
+
+#ifdef TEST_NNMULT
+#define NNMULT_DIM 128
+ test1 = new q7_t[NNMULT_DIM*2];
+ test2 = new q15_t[NNMULT_DIM*2];
+ test3 = new q7_t[NNMULT_DIM*2];
+ test4 = new q15_t[NNMULT_DIM*2];
+
+ q7_t * mult_out_q7 = test3;
+ q7_t * mult_ref_q7 = test3 + NNMULT_DIM;
+ q15_t * mult_out_q15 = test4;
+ q15_t * mult_ref_q15 = test4 + NNMULT_DIM;
+
+ for (int i=0;i<NNMULT_DIM*2;i++) {
+ test1[i] = (rand() % 256 - 128);
+ test2[i] = (rand() % 65536 - 32768);
+ }
+
+ // Test q7
+ arm_nn_mult_q7(test1, test1+NNMULT_DIM, mult_out_q7, 5, NNMULT_DIM);
+
+ arm_nn_mult_q7_ref(test1, test1+NNMULT_DIM, mult_ref_q7, 5, NNMULT_DIM);
+
+ verify_results_q7(mult_out_q7, mult_ref_q7, NNMULT_DIM);
+
+ arm_nn_mult_q7(test1, test1+NNMULT_DIM, mult_out_q7, 9, NNMULT_DIM);
+
+ arm_nn_mult_q7_ref(test1, test1+NNMULT_DIM, mult_ref_q7, 9, NNMULT_DIM);
+
+ verify_results_q7(mult_out_q7, mult_ref_q7, NNMULT_DIM);
+
+ // Test q15
+ arm_nn_mult_q15(test2, test2+NNMULT_DIM, mult_out_q15, 13, NNMULT_DIM);
+
+ arm_nn_mult_q15_ref(test2, test2+NNMULT_DIM, mult_ref_q15, 13, NNMULT_DIM);
+
+ verify_results_q15(mult_out_q15, mult_ref_q15, NNMULT_DIM);
+
+ arm_nn_mult_q15(test2, test2+NNMULT_DIM, mult_out_q15, 18, NNMULT_DIM);
+
+ arm_nn_mult_q15_ref(test2, test2+NNMULT_DIM, mult_ref_q15, 18, NNMULT_DIM);
+
+ verify_results_q15(mult_out_q15, mult_ref_q15, NNMULT_DIM);
+
+#endif
+
+#ifdef TEST_SIGMOID
+
+#define SIGMOID_DIM 128
+
+ /* This part tests the running of sigmoid functions */
+
+ test1 = new q7_t[SIGMOID_DIM];
+ test2 = new q15_t[SIGMOID_DIM];
+ test3 = new q7_t[SIGMOID_DIM];
+ test4 = new q15_t[SIGMOID_DIM];
+
+ srand(1);
+
+ for (int i = 0; i < SIGMOID_DIM; i++)
+ {
+ test1[i] = (rand() % 256 - 128);
+ test2[i] = (rand() % 65536 - 32768);
+ test3[i] = test1[i];
+ test4[i] = test2[i];
+ }
+
+ arm_nn_activations_direct_q7(test3, SIGMOID_DIM, 3, ARM_SIGMOID);
+
+ for (int i = 0; i < SIGMOID_DIM; i++)
+ {
+ printf("in: %d out: %d\n", test1[i], test3[i]);
+ }
+
+ printf("start testing q15_t sigmoid\n\n");
+
+ arm_nn_activations_direct_q15(test4, SIGMOID_DIM, 3, ARM_SIGMOID);
+
+ for (int i = 0; i < SIGMOID_DIM; i++)
+ {
+ printf("in: %d out: %d\n", test2[i], test4[i]);
+ }
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+ delete[]test4;
+
+#endif
+
+#ifdef TEST_TANH
+
+#define TANH_DIM 128
+
+ /* This part tests the running of sigmoid functions */
+
+ test1 = new q7_t[TANH_DIM];
+ test2 = new q15_t[TANH_DIM];
+ test3 = new q7_t[TANH_DIM];
+ test4 = new q15_t[TANH_DIM];
+
+ srand(1);
+
+ for (int i = 0; i < TANH_DIM; i++)
+ {
+ test1[i] = (rand() % 256 - 128);
+ test2[i] = (rand() % 65536 - 32768);
+ test3[i] = test1[i];
+ test4[i] = test2[i];
+ }
+
+ arm_nn_activations_direct_q7(test3, TANH_DIM, 3, ARM_TANH);
+
+ printf("start testing q7_t tanh\n\n");
+
+ for (int i = 0; i < TANH_DIM; i++)
+ {
+ printf("in: %d out: %d\n", test1[i], test3[i]);
+ }
+
+ printf("start testing q15_t tanh\n\n");
+
+ arm_nn_activations_direct_q15(test4, TANH_DIM, 3, ARM_TANH);
+
+ for (int i = 0; i < TANH_DIM; i++)
+ {
+ printf("in: %d out: %d\n", test2[i], test4[i]);
+ }
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+ delete[]test4;
+
+#endif
+
+#ifdef TEST_POOL
+
+#define POOL_IM_DIM 32
+#define POOL_IM_CH 8
+
+ test1 = new q7_t[POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH * 2];
+ test2 = new q15_t[POOL_IM_DIM * POOL_IM_CH];
+ test3 = new q7_t[POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH];
+
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ test1[i] = (rand() % 256 - 128);
+ }
+
+ q7_t *img_in = test1 + POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH;
+ q7_t *pool_out_ref = test3;
+ q7_t *pool_out_opt = test3 + POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH / 2;
+
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ test3[i] = 0;
+ }
+
+ // copy over the img input
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ img_in[i] = test1[i];
+ }
+
+ initialize_results_q7(pool_out_ref, pool_out_opt, POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH);
+
+ printf("Start maxpool reference implementation\n");
+
+ arm_maxpool_q7_HWC_ref(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_ref);
+
+ // copy over the img input
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ img_in[i] = test1[i];
+ }
+
+ printf("Start maxpool opt implementation\n");
+
+ arm_maxpool_q7_HWC(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_opt);
+
+ verify_results_q7(pool_out_ref, pool_out_opt, POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH);
+
+ // copy over the img input
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ img_in[i] = test1[i];
+ }
+
+ // copy over the img input
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ img_in[i] = test1[i];
+ }
+
+ printf("Start avepool ref implementation\n");
+
+ arm_avepool_q7_HWC_ref(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_ref);
+
+ // copy over the img input
+ for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+ {
+ img_in[i] = test1[i];
+ }
+
+ printf("Start avepool opt implementation\n");
+
+ arm_avepool_q7_HWC(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_opt);
+
+ // special check here
+ bool if_ave_pool_match = true;
+ for (int i = 0; i < POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH; i++)
+ {
+ // we tolerate at most difference of 1 here because of rounding errors
+ if (pool_out_ref[i] - pool_out_opt[i] >= 2 || pool_out_opt[i] - pool_out_ref[i] >= 2)
+ {
+ printf("Output mismatch at %d, expected %d, actual %d\n", i, pool_out_ref[i], pool_out_opt[i]);
+ if_ave_pool_match = false;
+ }
+ }
+ if (if_ave_pool_match == true)
+ {
+ printf("Outputs match.\n");
+ }
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+
+#endif
+
+#ifdef TEST_RELU
+
+#define RELU_DIM 127
+
+ test1 = new q7_t[RELU_DIM];
+ test2 = new q15_t[RELU_DIM];
+ test3 = new q7_t[RELU_DIM];
+ test4 = new q15_t[RELU_DIM];
+
+ for (int i = 0; i < RELU_DIM; i++)
+ {
+ test1[i] = (rand() % 256 - 128);
+ test2[i] = (rand() % 65536 - 32768);
+ test3[i] = test1[i];
+ test4[i] = test2[i];
+ }
+
+ q7_t *relu_ref_data_q7 = test1;
+ q7_t *relu_opt_data_q7 = test3;
+ q15_t *relu_ref_data_q15 = test2;
+ q15_t *relu_opt_data_q15 = test4;
+
+ printf("Start ref relu q7 implementation\n");
+
+ arm_relu_q7_ref(relu_ref_data_q7, RELU_DIM);
+
+ printf("Start opt relu q7 implementation\n");
+
+ arm_relu_q7(relu_opt_data_q7, RELU_DIM);
+
+ verify_results_q7(relu_ref_data_q7, relu_opt_data_q7, RELU_DIM);
+
+ printf("Start ref relu q15 implementation\n");
+
+ arm_relu_q15_ref(relu_ref_data_q15, RELU_DIM);
+
+ printf("Start opt relu q15 implementation\n");
+
+ arm_relu_q15(relu_opt_data_q15, RELU_DIM);
+
+ verify_results_q15(relu_ref_data_q15, relu_opt_data_q15, RELU_DIM);
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+ delete[]test4;
+
+#endif
+
+#ifdef TEST_IP
+
+#define IP_ROW_DIM 127
+#define IP_COL_DIM 127
+
+ q7_t ip_weights[IP_ROW_DIM * IP_COL_DIM] = IP2_WEIGHT;
+ q7_t ip_q7_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_WEIGHT;
+ q7_t ip_q7_q15_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_q7_q15_WEIGHT;
+ q15_t ip_q15_weights[IP_ROW_DIM * IP_COL_DIM] = IP2_WEIGHT;
+ q15_t ip_q15_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_WEIGHT_Q15;
+
+ test1 = new q7_t[IP_COL_DIM + IP_ROW_DIM];
+ test2 = new q15_t[IP_COL_DIM];
+ test3 = new q7_t[IP_ROW_DIM * 3];
+ test4 = new q15_t[IP_COL_DIM + IP_ROW_DIM * 2];
+
+ for (int i = 0; i < IP_ROW_DIM + IP_COL_DIM; i++)
+ {
+ test1[i] = rand() % 256 - 100;
+ }
+ for (int i = 0; i < IP_ROW_DIM * 3; i++)
+ {
+ test3[i] = 0;
+ }
+
+ q7_t *ip_bias_q7 = test1 + IP_COL_DIM;
+
+ q7_t *ip_out_q7_ref = test3;
+ q7_t *ip_out_q7_opt = test3 + IP_ROW_DIM;
+ q7_t *ip_out_q7_opt_fast = test3 + 2 * IP_ROW_DIM;
+ q15_t *ip_out_q15_ref = test4 + IP_COL_DIM;
+ q15_t *ip_out_q15_opt = test4 + IP_COL_DIM + IP_ROW_DIM;
+
+ initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt, IP_ROW_DIM);
+ initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+ initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+ printf("Start ref q7 implementation\n");
+
+ arm_fully_connected_q7_ref(test1, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_ref, test2);
+
+ printf("Start q7 implementation\n");
+
+ arm_fully_connected_q7(test1, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_opt, test2);
+
+ verify_results_q7(ip_out_q7_ref, ip_out_q7_opt, IP_ROW_DIM);
+
+ printf("Start q7 ref opt implementation\n");
+
+ arm_fully_connected_q7_opt_ref(test1, ip_q7_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+ ip_out_q7_opt_fast, test2);
+
+ verify_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+ printf("Start q7 opt implementation\n");
+
+ arm_fully_connected_q7_opt(test1, ip_q7_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_opt_fast,
+ test2);
+
+ verify_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+ for (int i = 0; i < IP_ROW_DIM + IP_COL_DIM; i++)
+ {
+ test4[i] = (rand() % 65536 - 32768);
+ }
+
+ initialize_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start ref q15 implementation\n");
+
+ arm_fully_connected_q15_ref(test4, ip_q15_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_ref, NULL);
+
+ printf("Start q15 implementation\n");
+
+ arm_fully_connected_q15(test4, ip_q15_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt, NULL);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start ref opt q15 implementation\n");
+
+ arm_fully_connected_q15_opt_ref(test4, ip_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt,
+ NULL);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start opt q15 implementation\n");
+
+ arm_fully_connected_q15_opt(test4, ip_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt, NULL);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ initialize_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start ref q7_q15 implementation\n");
+
+ arm_fully_connected_mat_q7_vec_q15_ref(test4, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q15_ref,
+ test2);
+
+ printf("Start q7_q15 implementation\n");
+
+ arm_fully_connected_mat_q7_vec_q15(test4, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q15_opt,
+ test2);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start ref opt q7_q15 implementation\n");
+
+ arm_fully_connected_mat_q7_vec_q15_opt_ref(test4, ip_q7_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+ ip_out_q15_opt, test2);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ printf("Start opt q7_q15 implementation\n");
+
+ arm_fully_connected_mat_q7_vec_q15_opt(test4, ip_q7_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+ ip_out_q15_opt, test2);
+
+ verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+ delete[]test4;
+
+#endif
+
+#ifdef TEST_NONSQUARE
+
+/* Use RCONV to differential with square CONV */
+
+#define RCONV_IM_DIM_X 10
+#define RCONV_IM_DIM_Y 8
+#define RCONV_IM_CH 4
+#define RCONV_KER_DIM_X 5
+#define RCONV_KER_DIM_Y 3
+#define RCONV_STRIDE_X 1
+#define RCONV_STRIDE_Y 1
+#define RCONV_PADDING_X 2
+#define RCONV_PADDING_Y 1
+#define RCONV_OUT_CH 4
+#define RCONV_OUT_DIM_X 10
+#define RCONV_OUT_DIM_Y 8
+
+ test1 = new q7_t[RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH];
+ test2 = new q15_t[2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH];
+ test3 =
+ new q7_t[RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH];
+
+ for (int i = 0; i < RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH; i++)
+ {
+ test1[i] = rand() % 256 - 100;
+ }
+
+ for (int i = 0;
+ i < RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH; i++)
+ {
+ test3[i] = rand() % 256 - 100;
+ }
+
+ q7_t *rconv_weight_q7 = test1;
+ q7_t *rconv_bias_q7 = test1 + RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH;
+
+ q15_t *rconv_buf = test2;
+
+ q7_t *rconv_im_in_q7 = test3;
+ q7_t *rconv_im_out_ref_q7 = test3 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH;
+ q7_t *rconv_im_out_opt_q7 =
+ test3 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+
+ initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ printf("start conv q7 nonsquare ref implementation\n");
+ arm_convolve_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ printf("start conv q7 nonsquare opt implementation\n");
+ arm_convolve_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ printf("start conv q7 nonsquare ref implementation\n");
+ arm_convolve_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ printf("start conv q7 nonsquare basic implementation\n");
+ arm_convolve_HWC_q7_basic_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ printf("start 1x1 conv q7 nonsquare fast implementation\n");
+ arm_convolve_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, 1, 1, 0, 0, RCONV_STRIDE_X,
+ RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7, RCONV_OUT_DIM_X,
+ RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ printf("start 1x1 conv q7 nonsquare dedicated function implementation\n");
+ arm_convolve_1x1_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+ RCONV_OUT_CH, 1, 1, 0, 0, RCONV_STRIDE_X,
+ RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7, RCONV_OUT_DIM_X,
+ RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ printf("start depthwise separable conv q7 nonsquare ref implementation\n");
+ arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH,
+ rconv_weight_q7, RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y,
+ RCONV_PADDING_X, RCONV_PADDING_Y, RCONV_STRIDE_X, RCONV_STRIDE_Y,
+ rconv_bias_q7, 1, 7, rconv_im_out_ref_q7, RCONV_OUT_DIM_X,
+ RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ printf("start depthwise separable conv q7 nonsquare opt implementation\n");
+ arm_depthwise_separable_conv_HWC_q7_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH,
+ rconv_weight_q7, RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y,
+ RCONV_PADDING_X, RCONV_PADDING_Y, RCONV_STRIDE_X, RCONV_STRIDE_Y,
+ rconv_bias_q7, 1, 7, rconv_im_out_opt_q7, RCONV_OUT_DIM_X,
+ RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+
+ test2 = new q15_t[RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH]; // weights + bias
+ test4 = new q15_t[2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH //buffer
+ + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH]; // i/o
+
+ for (int i = 0; i < RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH; i++)
+ {
+ test2[i] = rand() % 256 - 100;
+ }
+
+ for (int i = 0;
+ i < 2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH
+ + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+ i++)
+ {
+ test4[i] = rand() % 256 - 100;
+ }
+
+ q15_t *rconv_weight_q15 = test2;
+ q15_t *rconv_bias_q15 = test2 + RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH;
+
+ rconv_buf = test4;
+
+ q15_t *rconv_im_in_q15 = test4 + 2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH;
+ q15_t *rconv_im_out_ref_q15 = rconv_im_in_q15 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH;
+ q15_t *rconv_im_out_opt_q15 = rconv_im_out_ref_q15 + RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+
+ initialize_results_q15(rconv_im_out_ref_q15, rconv_im_out_opt_q15, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ printf("start conv q15 nonsquare ref implementation\n");
+ arm_convolve_HWC_q15_nonsquare_ref(rconv_im_in_q15, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q15,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q15, 1, 7, rconv_im_out_ref_q15,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ printf("start conv q5 nonsquare opt implementation\n");
+ arm_convolve_HWC_q15_fast_nonsquare(rconv_im_in_q15, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q15,
+ RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+ RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q15, 1, 7, rconv_im_out_opt_q15,
+ RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+ verify_results_q15(rconv_im_out_ref_q15, rconv_im_out_opt_q15, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+ delete [] test2;
+ delete [] test4;
+#endif
+
+#ifdef TEST_CONV
+
+#define CONV_IM_DIM 16
+#define CONV_IM_CH 16
+#define CONV_KER_DIM 5
+#define CONV_OUT_CH 16
+#define CONV_OUT_DIM 16
+
+ test1 = new q7_t[CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH];
+ test2 =
+ new q15_t[CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+ 2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH];
+ test3 = new q7_t[CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH];
+ test4 = new q15_t[CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH];
+
+ for (int i = 0; i < CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH; i++)
+ {
+ test1[i] = rand() % 256 - 100;
+ }
+
+ for (int i = 0;
+ i <
+ CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+ 2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH; i++)
+ {
+ test2[i] = (rand() % 65536 - 32768);
+ }
+
+ for (int i = 0; i < CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH; i++)
+ {
+ test3[i] = rand() % 256 - 100;
+ }
+
+ for (int i = 0; i < CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH; i++)
+ {
+ test4[i] = (rand() % 65536 - 32768);
+ }
+
+ q7_t *conv_weight_q7 = test1;
+ q7_t *conv_bias_q7 = test1 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+
+ q15_t *conv_weight_q15 = test2;
+ q15_t *conv_buf = test2 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+ q15_t *conv_bias_q15 =
+ test2 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+ 2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+
+ q7_t *conv_im_in_q7 = test3;
+ q7_t *conv_im_out_ref_q7 = test3 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH;
+ q7_t *conv_im_out_opt_q7 =
+ test3 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH;
+
+ q15_t *conv_im_in_q15 = test4;
+ q15_t *conv_im_out_ref_q15 = test4 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH;
+ q15_t *conv_im_out_opt_q15 =
+ test4 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH;
+
+ initialize_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q7 ref implementation\n");
+
+ arm_convolve_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ printf("start q7 basic implementation\n");
+
+ arm_convolve_HWC_q7_basic(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q7 fast implementation\n");
+
+ arm_convolve_HWC_q7_fast(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ // testing with RGB
+ printf("start q7 ref implementation for RGB\n");
+
+ arm_convolve_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ printf("start q7 basic implementation for RGB\n");
+
+ arm_convolve_HWC_q7_basic(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q7 RGB implementation for RGB\n");
+
+ arm_convolve_HWC_q7_RGB(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ // testing q15
+ initialize_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q15 ref implementation\n");
+
+ arm_convolve_HWC_q15_ref(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_ref_q15,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ printf("start q15 basic implementation\n");
+
+ arm_convolve_HWC_q15_basic(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_opt_q15,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q15 fast implementation\n");
+
+ arm_convolve_HWC_q15_fast(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_opt_q15,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ // depthwise separable conv
+ initialize_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ printf("start q7 depthwise_separable_conv ref implementation\n");
+
+ arm_depthwise_separable_conv_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ printf("start q7 depthwise_separable_conv implementation\n");
+
+ arm_depthwise_separable_conv_HWC_q7(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+ CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+ CONV_OUT_DIM, conv_buf, NULL);
+
+ verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+ delete[]test1;
+ delete[]test2;
+ delete[]test3;
+ delete[]test4;
+
+#endif
+
+ test_pass = true;
+ test_index = 0;
+ while (test_flags[test_index] != -1) {
+ if (test_flags[test_index]) {
+ test_pass = false;
+ }
+ test_index ++;
+ }
+ if (test_pass) {
+ printf("All tests passed\n");
+ } else {
+ printf("Test failed passed\n");
+ }
+
+ return 0;
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.h b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.h new file mode 100644 index 0000000..2e33988 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.h @@ -0,0 +1,78 @@ +#ifndef _MAIN_H_
+#define _MAIN_H_
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "arm_math.h"
+
+#include "arm_nnfunctions.h"
+#include "ref_functions.h"
+
+extern int test_index;
+extern q7_t test_flags[50];
+
+void initialize_results_q7(q7_t * ref, q7_t * opt, int length)
+{
+ arm_fill_q7(0, ref, length);
+ arm_fill_q7(37, opt, length);
+}
+
+void initialize_results_q15(q15_t * ref, q15_t * opt, int length)
+{
+ arm_fill_q15(0, ref, length);
+ arm_fill_q15(0x5F5, opt, length);
+}
+
+void verify_results_q7(q7_t * ref, q7_t * opt, int length)
+{
+
+ bool if_match = true;
+
+ for (int i = 0; i < length; i++)
+ {
+ if (ref[i] != opt[i])
+ {
+ printf("Output mismatch at %d, expected %d, actual %d\r\n", i, ref[i], opt[i]);
+
+ if_match = false;
+ }
+ }
+
+ if (if_match == true)
+ {
+ printf("Outputs match.\r\n\r\n");
+ test_flags[test_index++] = 0;
+ } else {
+ test_flags[test_index++] = 1;
+ }
+
+}
+
+void verify_results_q15(q15_t * ref, q15_t * opt, int length)
+{
+
+ bool if_match = true;
+
+ for (int i = 0; i < length; i++)
+ {
+ if (ref[i] != opt[i])
+ {
+ printf("Output mismatch at %d, expected %d, actual %d\r\n", i, ref[i], opt[i]);
+
+ if_match = false;
+ }
+ }
+
+ if (if_match == true)
+ {
+ printf("Outputs match.\r\n\r\n");
+ test_flags[test_index++] = 0;
+ } else {
+ test_flags[test_index++] = 1;
+ }
+
+}
+
+#endif
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c new file mode 100644 index 0000000..fd447e5 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c @@ -0,0 +1,101 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_activations_q15.c
+ * Description: Q15 neural network activation function using direct table look-up
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+ /**
+ * @brief Q15 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ *
+ * @details
+ *
+ * This is the direct table look-up approach.
+ *
+ * Assume here the integer part of the fixed-point is <= 3.
+ * More than 3 just not making much sense, makes no difference with
+ * saturation followed by any of these activation functions.
+ */
+
+void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+ uint16_t i = size;
+ q15_t *pIn = data;
+ q15_t *pOut = data;
+ uint16_t shift_size = 8 + 3 - int_width;
+ uint32_t bit_mask = 0x7FF >> int_width;
+ uint32_t full_frac = bit_mask + 1;
+ const q15_t *lookup_table;
+
+ switch (type)
+ {
+ case ARM_SIGMOID:
+ lookup_table = sigmoidTable_q15;
+ break;
+ case ARM_TANH:
+ default:
+ lookup_table = tanhTable_q15;
+ break;
+ }
+
+ while (i)
+ {
+ q15_t out;
+ q15_t in = *pIn++;
+ q15_t frac = (uint32_t) in & bit_mask;
+ q15_t value = lookup_table[__USAT(in >> shift_size, 8)];
+ q15_t value2 = lookup_table[__USAT(1 + (in >> shift_size), 8)];
+
+ /* doing the interpolation here for better accuracy */
+ out = ((q31_t) (full_frac - frac) * value + (q31_t) value2 * frac) >> shift_size;
+
+ *pOut++ = out;
+ i--;
+ }
+
+}
+
+/**
+ * @} end of Acti group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c new file mode 100644 index 0000000..2953bd5 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c @@ -0,0 +1,91 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_activations_q7.c
+ * Description: Q7 neural network activation function using direct table look-up
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+ /**
+ * @brief Q7 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ *
+ * @details
+ *
+ * This is the direct table look-up approach.
+ *
+ * Assume here the integer part of the fixed-point is <= 3.
+ * More than 3 just not making much sense, makes no difference with
+ * saturation followed by any of these activation functions.
+ */
+
+void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+ uint16_t i = size;
+ q7_t *pIn = data;
+ q7_t *pOut = data;
+ q7_t in;
+ q7_t out;
+ uint16_t shift_size = 3 - int_width;
+ const q7_t *lookup_table;
+ switch (type)
+ {
+ case ARM_SIGMOID:
+ lookup_table = sigmoidTable_q7;
+ break;
+ case ARM_TANH:
+ default:
+ lookup_table = tanhTable_q7;
+ break;
+ }
+ while (i)
+ {
+ in = *pIn++;
+ out = lookup_table[(uint8_t) (in >> shift_size)];
+ *pOut++ = out;
+ i--;
+ }
+}
+
+/**
+ * @} end of Acti group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c new file mode 100644 index 0000000..6a1b907 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c @@ -0,0 +1,106 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_relu_q15.c
+ * Description: Q15 version of ReLU
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+ /**
+ * @brief Q15 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ *
+ * @details
+ *
+ * Optimized relu with QSUB instructions.
+ *
+ */
+
+void arm_relu_q15(q15_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ uint16_t i = size >> 1;
+ q15_t *pIn = data;
+ q15_t *pOut = data;
+ q31_t in;
+ q31_t buf;
+ q31_t mask;
+
+ while (i)
+ {
+ in = *__SIMD32(pIn)++;
+
+ /* extract the first bit */
+ buf = __ROR(in & 0x80008000, 15);
+
+ /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+ mask = __QSUB16(0x00000000, buf);
+
+ *__SIMD32(pOut)++ = in & (~mask);
+ i--;
+ }
+
+ if (size & 0x1)
+ {
+ if (*pIn < 0)
+ {
+ *pIn = 0;
+ }
+ pIn++;
+ }
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t i;
+
+ for (i = 0; i < size; i++)
+ {
+ if (data[i] < 0)
+ data[i] = 0;
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c new file mode 100644 index 0000000..caa027b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c @@ -0,0 +1,110 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_relu_q7.c
+ * Description: Q7 version of ReLU
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+ /**
+ * @brief Q7 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ *
+ * @details
+ *
+ * Optimized relu with QSUB instructions.
+ *
+ */
+
+void arm_relu_q7(q7_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ uint16_t i = size >> 2;
+ q7_t *pIn = data;
+ q7_t *pOut = data;
+ q31_t in;
+ q31_t buf;
+ q31_t mask;
+
+ while (i)
+ {
+ in = *__SIMD32(pIn)++;
+
+ /* extract the first bit */
+ buf = __ROR(in & 0x80808080, 7);
+
+ /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+ mask = __QSUB8(0x00000000, buf);
+
+ *__SIMD32(pOut)++ = in & (~mask);
+ i--;
+ }
+
+ i = size & 0x3;
+ while (i)
+ {
+ if (*pIn < 0)
+ {
+ *pIn = 0;
+ }
+ pIn++;
+ i--;
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ uint16_t i;
+
+ for (i = 0; i < size; i++)
+ {
+ if (data[i] < 0)
+ data[i] = 0;
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c new file mode 100644 index 0000000..4c69e7c --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c @@ -0,0 +1,235 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_1x1_HWC_q7_fast_nonsquare.c
+ * Description: Fast Q7 version of 1x1 convolution (non-square shape)
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+ * and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise
+ * separable convolution.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ *
+ * [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
+ * https://arxiv.org/abs/1704.04861
+ */
+
+arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x;
+ int16_t i_ch_out;
+
+ /* -----------------------
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+ || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer,
+ ch_im_in);
+ pBuffer += ch_im_in;
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* check if there is left-over for compute */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+ {
+ q31_t sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+ q15_t *pB = bufferA;
+ /* basically each time it process 4 entries */
+ uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+ while (colCnt)
+ {
+
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+ pOut++;
+
+ }
+
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ int i, j, k, l, m, n;
+ int conv_out;
+ int in_row, in_col;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+ || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+ conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ // if-for implementation
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c new file mode 100644 index 0000000..ee08d74 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c @@ -0,0 +1,207 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q15_basic.c
+ * Description: Q15 version of convolution
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Basic Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * This basic version is designed to work for any input tensor and weight
+ * dimension.
+ */
+
+arm_status
+arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ uint16_t im2col_out_pixel_index = 0;
+ q15_t *pBuffer = bufferA;
+ q15_t *pOut = Im_out;
+ q15_t *im_buffer = bufferA;
+ const q15_t *pA;
+ int i;
+
+ /* This part implements the im2col function */
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* Filling 0 for out-of-bound paddings */
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+ memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ pA = wt;
+ for (i = 0; i < ch_im_out; i++)
+ {
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q15_t *pB = im_buffer;
+ uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+ while (colCnt)
+ {
+ q31_t inA1 = *__SIMD32(pA)++;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inA2 = *__SIMD32(pA)++;
+ q31_t inB2 = *__SIMD32(pB)++;
+
+ sum = __SMLAD(inA1, inB1, sum);
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut = (q15_t) __SSAT((sum >> out_shift), 16);
+ pOut++;
+ }
+
+ /* counter reset */
+ pBuffer = im_buffer;
+ im2col_out_pixel_index++;
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+ conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c new file mode 100644 index 0000000..a02aaa0 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c @@ -0,0 +1,255 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q15_fast.c
+ * Description: Fast Q15 version of convolution
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Fast Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in is multiple of 2
+ *
+ * ch_im_out is multipe of 2
+ *
+ */
+
+arm_status
+arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ q15_t *pBuffer = bufferA;
+ q15_t *im_buffer = bufferA;
+ q15_t *pOut = Im_out;
+
+ if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ /* This part implements the im2col function */
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+ memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (i_out_x & 0x1)
+ {
+ int i;
+ /* initialize the matrix pointers for A */
+ const q15_t *pA = wt;
+
+ /* set up the second output pointers */
+ q15_t *pOut2 = pOut + ch_im_out;
+
+ /* this loop over rows in A */
+ for (i = 0; i < ch_im_out; i += 2)
+ {
+ /* setup pointers for B */
+ q15_t *pB = im_buffer;
+ const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel;
+
+ /* aling the second pointer for A */
+ const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel;
+
+ /* init the sum with bias */
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 1;
+ /* accumulate over the vector */
+ while (colCnt)
+ {
+ q31_t inA1 = *__SIMD32(pA)++;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inA2 = *__SIMD32(pA2)++;
+ q31_t inB2 = *__SIMD32(pB2)++;
+
+ sum = __SMLAD(inA1, inB1, sum);
+ sum2 = __SMLAD(inA1, inB2, sum2);
+ sum3 = __SMLAD(inA2, inB1, sum3);
+ sum4 = __SMLAD(inA2, inB2, sum4);
+
+ colCnt--;
+ } /* while over colCnt */
+ colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1;
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ q15_t inA2 = *pA2++;
+ q15_t inB2 = *pB2++;
+
+ sum += inA1 * inB1;
+ sum2 += inA1 * inB2;
+ sum3 += inA2 * inB1;
+ sum4 += inA2 * inB2;
+ colCnt--;
+ } /* while over colCnt */
+ *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+ *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+ *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+ *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+ /* skip the row computed with A2 */
+ pA += ch_im_in * dim_kernel * dim_kernel;
+ } /* for over ch_im_out */
+
+ pOut += ch_im_out;
+ /* counter reset */
+ pBuffer = im_buffer;
+ }
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+ conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c new file mode 100644 index 0000000..14d9130 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c @@ -0,0 +1,265 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q15_fast.c
+ * Description: Fast Q15 version of convolution
+ *
+ * $Date: 24. May 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Fast Q15 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in is multiple of 2
+ *
+ * ch_im_out is multipe of 2
+ *
+ */
+
+arm_status
+arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ q15_t *pBuffer = bufferA;
+ q15_t *im_buffer = bufferA;
+ q15_t *pOut = Im_out;
+
+ if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ /* This part implements the im2col function */
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+ memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (i_out_x & 0x1)
+ {
+ int i;
+ /* initialize the matrix pointers for A */
+ const q15_t *pA = wt;
+
+ /* set up the second output pointers */
+ q15_t *pOut2 = pOut + ch_im_out;
+
+ /* this loop over rows in A */
+ for (i = 0; i < ch_im_out; i += 2)
+ {
+ /* setup pointers for B */
+ q15_t *pB = im_buffer;
+ const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+ /* aling the second pointer for A */
+ const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+ /* init the sum with bias */
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1;
+ /* accumulate over the vector */
+ while (colCnt)
+ {
+ q31_t inA1 = *__SIMD32(pA)++;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inA2 = *__SIMD32(pA2)++;
+ q31_t inB2 = *__SIMD32(pB2)++;
+
+ sum = __SMLAD(inA1, inB1, sum);
+ sum2 = __SMLAD(inA1, inB2, sum2);
+ sum3 = __SMLAD(inA2, inB1, sum3);
+ sum4 = __SMLAD(inA2, inB2, sum4);
+
+ colCnt--;
+ } /* while over colCnt */
+ colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1;
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ q15_t inA2 = *pA2++;
+ q15_t inB2 = *pB2++;
+
+ sum += inA1 * inB1;
+ sum2 += inA1 * inB2;
+ sum3 += inA2 * inB1;
+ sum4 += inA2 * inB2;
+ colCnt--;
+ } /* while over colCnt */
+ *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+ *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+ *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+ *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+ /* skip the row computed with A2 */
+ pA += ch_im_in * dim_kernel_y * dim_kernel_x;
+ } /* for over ch_im_out */
+
+ pOut += ch_im_out;
+ /* counter reset */
+ pBuffer = im_buffer;
+ }
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+ conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c new file mode 100644 index 0000000..e53c6f9 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c @@ -0,0 +1,279 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q7_RGB.c
+ * Description: Q7 version of convolution for RGB image
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Q7 convolution function for RGB image
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in equals 3
+ *
+ * This kernel is written exclusively for convolution with ch_im_in
+ * equals 3. This applies on the first layer of CNNs which has input
+ * image with RGB format.
+ */
+
+arm_status
+arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out, const uint16_t dim_im_out, q15_t * bufferA, q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ /*
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ // check if number of input channels is 3
+ if (ch_im_in != 3)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+ // This part implements the im2col function
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */
+ *__SIMD32(pBuffer) = 0x0;
+ *(pBuffer + 2) = 0;
+ pBuffer += 3;
+ } else
+ {
+ /*
+ * Equivalent to:
+ * arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3);
+ */
+
+ const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3;
+ q31_t buf = *__SIMD32(pPixel);
+
+ union arm_nnword top;
+ union arm_nnword bottom;
+
+ top.word = __SXTB16(buf);
+ bottom.word = __SXTB16(__ROR(buf, 8));
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ /*
+ * little-endian, | omit | 3rd | 2nd | 1st |
+ * MSB LSB
+ * top | 3rd | 1st |; bottom | omit | 2nd |
+ *
+ * version 1, need to swap 2nd and 3rd weight
+ * *__SIMD32(pBuffer) = top.word;
+ * *(pBuffer+2) = bottom.half_words[0];
+ *
+ * version 2, no weight shuffling required
+ */
+ *pBuffer++ = top.half_words[0];
+ *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0);
+#else
+ /*
+ * big-endian, | 1st | 2nd | 3rd | omit |
+ * MSB LSB
+ * top | 2nd | omit |; bottom | 1st | 3rd |
+ *
+ * version 1, need to swap 2nd and 3rd weight
+ * *__SIMD32(pBuffer) = bottom.word;
+ * *(pBuffer+2) = top.half_words[1];
+ *
+ * version 2, no weight shuffling required
+ */
+ *pBuffer++ = bottom.half_words[0];
+ *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0);
+#endif
+ pBuffer += 2;
+ }
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+ ch_im_out,
+ 3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* left-over because odd number of output pixels */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ int i;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q15_t *pB = bufferA;
+ /* basically each time it process 4 entries */
+ uint16_t colCnt = 3 * dim_kernel * dim_kernel >> 2;
+
+ while (colCnt)
+ {
+
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = 3 * dim_kernel * dim_kernel & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ }
+ }
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ // check if number of input channels is 3
+ if (ch_im_in != 3)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+ conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ /* if-for implementation */
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return (ARM_MATH_SUCCESS);
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c new file mode 100644 index 0000000..7c9ec65 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c @@ -0,0 +1,230 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q7_basic.c
+ * Description: Q7 version of convolution
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Basic Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * This basic version is designed to work for any input tensor and weight
+ * dimension.
+ */
+
+arm_status
+arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ /*
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ /* This part implements the im2col function */
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* Filling 0 for out-of-bound paddings */
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ /* Copying the pixel data to column */
+ arm_q7_to_q15_no_shift((q7_t *)
+ Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ /* Computation is filed for every 2 columns */
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+ ch_im_out,
+ ch_im_in *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* left-over because odd number of output pixels */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ int i;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ /* Load the accumulator with bias first */
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+ /* Point to the beging of the im2col buffer */
+ q15_t *pB = bufferA;
+
+ /* Each time it process 4 entries */
+ uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ }
+ }
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+ conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ // if-for implementation
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c new file mode 100644 index 0000000..24356d9 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c @@ -0,0 +1,228 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q7_basic.c
+ * Description: Q7 version of convolution
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Basic Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ */
+
+arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ /*
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ /* This part implements the im2col function */
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* Filling 0 for out-of-bound paddings */
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ /* Copying the pixel data to column */
+ arm_q7_to_q15_no_shift((q7_t *)
+ Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ /* Computation is filed for every 2 columns */
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+ ch_im_out,
+ ch_im_in *
+ dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut);
+
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* left-over because odd number of output pixels */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ int i;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ /* Load the accumulator with bias first */
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+ /* Point to the beging of the im2col buffer */
+ q15_t *pB = bufferA;
+
+ /* Each time it process 4 entries */
+ uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2;
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ }
+ }
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+ conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ // if-for implementation
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel_y * dim_kernel_x +
+ (m * dim_kernel_x + n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c new file mode 100644 index 0000000..e2d469f --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c @@ -0,0 +1,408 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q7_fast.c
+ * Description: Fast Q7 version of convolution
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+ /**
+ * @brief Fast Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap )
+ *
+ * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel )
+ *
+ * The im2col converts the Q7 tensor input into Q15 column, which is stored in
+ * bufferA. There is reordering happenning during this im2col process with
+ * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and
+ * third elements are swapped.
+ *
+ * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the
+ * GEMM computation with the reordered columns.
+ *
+ * To speed-up the determination of the padding condition, we split the
+ * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}.
+ * This reduces the total number of boundary condition checks and improves
+ * the data copying performance.
+ */
+
+arm_status
+arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ /*
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ /*
+ * Here we split the entire matrix into three regions depending on the padding situation
+ * Top: i_out_y from 0 to padding - 1
+ * Middle: i_out_y from padding to dim_im_out-padding-1
+ * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+ */
+
+ /* top part */
+ for (i_out_y = 0; i_out_y < padding; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift
+ ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+ bufferA,
+ ch_im_out,
+ ch_im_in
+ *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* middle part, here we also divide the x into left, mid and right */
+ for (; i_out_y < dim_im_out - padding; i_out_y++)
+ {
+
+ /* left part */
+ for (i_out_x = 0; i_out_x < padding; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift
+ ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+ bufferA,
+ ch_im_out,
+ ch_im_in
+ *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+
+ /* mid part */
+ for (; i_out_x < dim_im_out - padding; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in
+ +
+ (i_ker_y *
+ dim_im_in +
+ i_out_x *
+ stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel);
+ pBuffer += ch_im_in * dim_kernel;
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+ bufferA,
+ ch_im_out,
+ ch_im_in
+ *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+
+ /* right part */
+ for (; i_out_x < dim_im_out; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift
+ ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+ bufferA,
+ ch_im_out,
+ ch_im_in
+ *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ for (; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift
+ ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+ bufferA,
+ ch_im_out,
+ ch_im_in
+ *
+ dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* check if there is left-over for compute */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ int i;
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+ q15_t *pB = bufferA;
+ /* each time it process 4 entries */
+ uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+ while (colCnt)
+ {
+
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+ pOut++;
+
+ }
+
+ }
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ uint16_t i, j, k, l, m, n;
+ int conv_out;
+ signed char in_row, in_col;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out; j++)
+ {
+ for (k = 0; k < dim_im_out; k++)
+ {
+ conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel; m++)
+ {
+ for (n = 0; n < dim_kernel; n++)
+ {
+ // if-for implementation
+ in_row = stride * j + m - padding;
+ in_col = stride * k + n - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out +=
+ Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+ l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+ n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c new file mode 100644 index 0000000..6dc6f0b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c @@ -0,0 +1,379 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_convolve_HWC_q7_fast_nonsquare.c
+ * Description: Fast Q7 version of convolution (non-sqaure shape)
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+
+arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+ /* -----------------------
+ * Here we use bufferA as q15_t internally as computation are done with q15_t level
+ * im2col are done to output in q15_t format from q7_t input
+ */
+
+ q15_t *pBuffer = bufferA;
+ q7_t *pOut = Im_out;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ /*
+ * Here we split the entire matrix into three regions depending on the padding situation
+ * Top: i_out_y from 0 to padding - 1
+ * Middle: i_out_y from padding to dim_im_out-padding-1
+ * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+ */
+
+ /* top part */
+ for (i_out_y = 0; i_out_y < padding_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+ i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+ pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+ bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* middle part, here we also divide the x into left, mid and right */
+ for (; i_out_y < dim_im_out_y - padding_y; i_out_y++)
+ {
+
+ /* left part */
+ for (i_out_x = 0; i_out_x < padding_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+ i_ker_x++)
+ {
+ if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+ pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+ bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+
+ /* mid part */
+ for (; i_out_x < dim_im_out_x - padding_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in +
+ (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in,
+ pBuffer, ch_im_in * dim_kernel_x);
+ pBuffer += ch_im_in * dim_kernel_x;
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+ bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+
+ /* right part */
+ for (; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+ i_ker_x++)
+ {
+ if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+ pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+ bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ for (; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ /* This part implements the im2col function */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+ i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q15(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+ } else
+ {
+ arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+ pBuffer, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+ {
+ pOut =
+ arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+ bias_shift, out_shift, bias, pOut);
+ /* counter reset */
+ pBuffer = bufferA;
+ }
+ }
+ }
+
+ /* check if there is left-over for compute */
+ if (pBuffer != bufferA)
+ {
+ const q7_t *pA = wt;
+ int i;
+ for (i = 0; i < ch_im_out; i++)
+ {
+ q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ q15_t *pB = bufferA;
+ /* basically each time it process 4 entries */
+ uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+ while (colCnt)
+ {
+
+ q31_t inA1, inA2;
+ q31_t inB1, inB2;
+
+ pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+ inB1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA1, inB1, sum);
+ inB2 = *__SIMD32(pB)++;
+ sum = __SMLAD(inA2, inB2, sum);
+
+ colCnt--;
+ }
+ colCnt = (ch_im_in * dim_kernel_y * dim_kernel_x) & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ sum += inA1 * inB1;
+ colCnt--;
+ }
+ *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+ pOut++;
+
+ }
+
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ int i, j, k, l, m, n;
+ int conv_out;
+ int in_row, in_col;
+
+ if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+ {
+ /* check if the input dimension meets the constraints */
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i = 0; i < ch_im_out; i++)
+ {
+ for (j = 0; j < dim_im_out_y; j++)
+ {
+ for (k = 0; k < dim_im_out_x; k++)
+ {
+ conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ for (m = 0; m < dim_kernel_y; m++)
+ {
+ for (n = 0; n < dim_kernel_x; n++)
+ {
+ /* if-for implementation */
+ in_row = stride_y * j + m - padding_y;
+ in_col = stride_x * k + n - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ for (l = 0; l < ch_im_in; l++)
+ {
+ conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+ wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l];
+ }
+ }
+ }
+ }
+ Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c new file mode 100644 index 0000000..705fa6a --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c @@ -0,0 +1,418 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_depthwise_separable_conv_HWC_q7.c
+ * Description: Q7 depthwise separable convolution function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in equals ch_im_out
+ *
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_out_y, i_out_x;
+ int16_t i_ker_y, i_ker_x;
+ q7_t *colBuffer = (q7_t *) bufferA;
+ q7_t *pBuffer = colBuffer;
+ const q7_t *pBias = bias;
+ q7_t *pOut = Im_out;
+ uint16_t rowCnt;
+ uint16_t row_shift;
+
+ /* do some checking here, basically ch_im_in == ch_im_out */
+ if (ch_im_in != ch_im_out)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ /* we first do im2col here */
+ for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+ {
+ /* arm_fill_q7(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, ch_im_in);
+ } else
+ {
+ /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+ memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ /* we will do the computation here for each channel */
+ rowCnt = ch_im_out >> 2;
+ row_shift = 0;
+ pBias = bias;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = (dim_kernel * dim_kernel) >> 1;
+ q7_t *pB = colBuffer + row_shift;
+ const q7_t *pA = wt + row_shift;
+ row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2, inB1, inB2, opA, opB;
+
+ inB1 = *__SIMD32(pB);
+ pB += ch_im_in;
+ opB = *__SIMD32(pB);
+ pB += ch_im_in;
+ inB2 = __PKHTB(opB, inB1, 16);
+ inB1 = __PKHBT(inB1, opB, 16);
+ inA1 = *__SIMD32(pA);
+ pA += ch_im_in;
+ opB = *__SIMD32(pA);
+ pA += ch_im_in;
+ inA2 = __PKHTB(opB, inA1, 16);
+ inA1 = __PKHBT(inA1, opB, 16);
+ opA = __SXTB16(inA1);
+ opB = __SXTB16(inB1);
+ sum = __SMLAD(opA, opB, sum);
+ opA = __SXTB16(__ROR(inA1, 8));
+ opB = __SXTB16(__ROR(inB1, 8));
+ sum2 = __SMLAD(opA, opB, sum2);
+ opA = __SXTB16(inA2);
+ opB = __SXTB16(inB2);
+ sum3 = __SMLAD(opA, opB, sum3);
+ opA = __SXTB16(__ROR(inA2, 8));
+ opB = __SXTB16(__ROR(inB2, 8));
+ sum4 = __SMLAD(opA, opB, sum4);
+ colCnt--;
+ }
+#else
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2, inB1, inB2, opA, opB;
+
+ inB1 = *__SIMD32(pB);
+ pB += ch_im_in;
+ opB = *__SIMD32(pB);
+ pB += ch_im_in;
+ inB2 = __PKHBT(opB, inB1, 16);
+ inB1 = __PKHTB(inB1, opB, 16);
+ inA1 = *__SIMD32(pA);
+ pA += ch_im_in;
+ opB = *__SIMD32(pA);
+ pA += ch_im_in;
+ inA2 = __PKHBT(opB, inA1, 16);
+ inA1 = __PKHTB(inA1, opB, 16);
+ opA = __SXTB16(inA1);
+ opB = __SXTB16(inB1);
+ sum2 = __SMLAD(opA, opB, sum2);
+ opA = __SXTB16(__ROR(inA1, 8));
+ opB = __SXTB16(__ROR(inB1, 8));
+ sum = __SMLAD(opA, opB, sum);
+ opA = __SXTB16(inA2);
+ opB = __SXTB16(inB2);
+ sum4 = __SMLAD(opA, opB, sum4);
+ opA = __SXTB16(__ROR(inA2, 8));
+ opB = __SXTB16(__ROR(inB2, 8));
+ sum3 = __SMLAD(opA, opB, sum3);
+ colCnt--;
+ }
+
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ /*
+ * r0 r1 r2 r3 r4 r5
+ * inA1, inA2, inB1, inB2, opA, opB
+ */
+
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r2, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "ldr.w r5, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "pkhtb r3, r5, r2, ASR #16\n"
+ "pkhbt r2, r2, r5, LSL #16\n"
+ "ldr.w r0, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "ldr.w r5, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "pkhtb r1, r5, r0, ASR #16\n"
+ "pkhbt r0, r0, r5, LSL #16\n"
+ "sxtb16 r4, r0\n"
+ "sxtb16 r5, r2\n"
+ "smlad %[sum], r4, r5, %[sum]\n"
+ "mov.w r4, r0, ror #8\n"
+ "mov.w r5, r2, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum2], r4, r5, %[sum2]\n"
+ "sxtb16 r4, r1\n"
+ "sxtb16 r5, r3\n"
+ "smlad %[sum3], r4, r5, %[sum3]\n"
+ "mov.w r4, r1, ror #8\n"
+ "mov.w r5, r3, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum4], r4, r5, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum]
+ "+r"(sum),[sum2] "+r"(sum2),
+ [sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),
+ [pA] "+r"(pA):[colCnt]
+ "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+ /*
+ * r0 r1 r2 r3 r4 r5
+ * inA1, inA2, inB1, inB2, opA, opB
+ */
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r2, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "ldr.w r5, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "pkhbt r3, r5, r2, LSL #16\n"
+ "pkhtb r2, r2, r5, ASR #16\n"
+ "ldr.w r0, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "ldr.w r5, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "pkhbt r1, r5, r0, LSL #16\n"
+ "pkhtb r0, r0, r5, ASR #16\n"
+ "sxtb16 r4, r0\n"
+ "sxtb16 r5, r2\n"
+ "smlad %[sum2], r4, r5, %[sum2]\n"
+ "mov.w r4, r0, ror #8\n"
+ "mov.w r5, r2, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum], r4, r5, %[sum]\n"
+ "sxtb16 r4, r1\n"
+ "sxtb16 r5, r3\n"
+ "smlad %[sum4], r4, r5, %[sum4]\n"
+ "mov.w r4, r1, ror #8\n"
+ "mov.w r5, r3, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum3], r4, r5, %[sum3]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum]
+ "+r"(sum),[sum2] "+r"(sum2),
+ [sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),
+ [pA] "+r"(pA):[colCnt]
+ "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#endif /* USE_INTRINSIC */
+
+ colCnt = (dim_kernel * dim_kernel) & 0x1;
+ while (colCnt)
+ {
+ union arm_nnword inA, inB;
+ inA.word = *__SIMD32(pA);
+ pA += ch_im_in;
+ inB.word = *__SIMD32(pB);
+ pB += ch_im_in;
+ sum += inA.bytes[0] * inB.bytes[0];
+ sum2 += inA.bytes[1] * inB.bytes[1];
+ sum3 += inA.bytes[2] * inB.bytes[2];
+ sum4 += inA.bytes[3] * inB.bytes[3];
+ colCnt--;
+ }
+
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ rowCnt--;
+ }
+
+ rowCnt = ch_im_out & 0x3;
+ while (rowCnt)
+ {
+ q7_t *pB = colBuffer + row_shift;
+ const q7_t *pA = wt + row_shift;
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = (dim_kernel * dim_kernel);
+
+ row_shift += 1;
+
+ while (colCnt)
+ {
+ q7_t A1 = *pA;
+ q7_t B1 = *pB;
+ pA += ch_im_in;
+ pB += ch_im_in;
+ sum += A1 * B1;
+
+ colCnt--;
+ }
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ rowCnt--;
+ }
+
+ /* clear counter and pointers */
+ pBuffer = colBuffer;
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ int i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y;
+ int conv_out;
+
+ /* do some checking here, basically ch_im_in == ch_im_out */
+ if (ch_im_in != ch_im_out)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+ {
+ for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+ {
+ // for each output
+ conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+ for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++)
+ {
+ for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++)
+ {
+ int in_row = stride * i_out_y + i_ker_y - padding;
+ int in_col = stride * i_out_x + i_ker_x - padding;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+ {
+ conv_out +=
+ Im_in[(in_row *
+ dim_im_in +
+ in_col) *
+ ch_im_in +
+ i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out];
+ }
+ }
+ }
+ Im_out[(i_out_y * dim_im_out +
+ i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c new file mode 100644 index 0000000..5989304 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c @@ -0,0 +1,411 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_depthwise_separable_conv_HWC_q7_nonsquare.c
+ * Description: Q7 depthwise separable convolution function (non-square shape)
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function (non-square shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding sizes x
+ * @param[in] padding_y padding sizes y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+/*
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ *
+ */
+
+ int16_t i_out_y, i_out_x;
+ int16_t i_ker_y, i_ker_x;
+ q7_t *colBuffer = (q7_t *) bufferA;
+ q7_t *pBuffer = colBuffer;
+ const q7_t *pBias = bias;
+ q7_t *pOut = Im_out;
+ uint16_t rowCnt;
+ uint16_t row_shift;
+
+ /* do some checking here, basically ch_im_in == ch_im_out */
+ if (ch_im_in != ch_im_out)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ /* we first do im2col here */
+ for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+ i_ker_y++)
+ {
+ for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+ i_ker_x++)
+ {
+ if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+ {
+ /* arm_fill_q7(0, pBuffer, ch_im_in); */
+ memset(pBuffer, 0, ch_im_in);
+ } else
+ {
+ /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+ memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, ch_im_in);
+ }
+ pBuffer += ch_im_in;
+ }
+ }
+
+ /* we will do the computation here for each channel */
+ rowCnt = ch_im_out >> 2;
+ row_shift = 0;
+ pBias = bias;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = (dim_kernel_x * dim_kernel_y) >> 1;
+ q7_t *pB = colBuffer + row_shift;
+ const q7_t *pA = wt + row_shift;
+ row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2, inB1, inB2, opA, opB;
+
+ inB1 = *__SIMD32(pB);
+ pB += ch_im_in;
+ opB = *__SIMD32(pB);
+ pB += ch_im_in;
+ inB2 = __PKHTB(opB, inB1, 16);
+ inB1 = __PKHBT(inB1, opB, 16);
+ inA1 = *__SIMD32(pA);
+ pA += ch_im_in;
+ opB = *__SIMD32(pA);
+ pA += ch_im_in;
+ inA2 = __PKHTB(opB, inA1, 16);
+ inA1 = __PKHBT(inA1, opB, 16);
+ opA = __SXTB16(inA1);
+ opB = __SXTB16(inB1);
+ sum = __SMLAD(opA, opB, sum);
+ opA = __SXTB16(__ROR(inA1, 8));
+ opB = __SXTB16(__ROR(inB1, 8));
+ sum2 = __SMLAD(opA, opB, sum2);
+ opA = __SXTB16(inA2);
+ opB = __SXTB16(inB2);
+ sum3 = __SMLAD(opA, opB, sum3);
+ opA = __SXTB16(__ROR(inA2, 8));
+ opB = __SXTB16(__ROR(inB2, 8));
+ sum4 = __SMLAD(opA, opB, sum4);
+ colCnt--;
+ }
+#else
+
+ while (colCnt)
+ {
+ q31_t inA1, inA2, inB1, inB2, opA, opB;
+
+ inB1 = *__SIMD32(pB);
+ pB += ch_im_in;
+ opB = *__SIMD32(pB);
+ pB += ch_im_in;
+ inB2 = __PKHBT(opB, inB1, 16);
+ inB1 = __PKHTB(inB1, opB, 16);
+ inA1 = *__SIMD32(pA);
+ pA += ch_im_in;
+ opB = *__SIMD32(pA);
+ pA += ch_im_in;
+ inA2 = __PKHBT(opB, inA1, 16);
+ inA1 = __PKHTB(inA1, opB, 16);
+ opA = __SXTB16(inA1);
+ opB = __SXTB16(inB1);
+ sum2 = __SMLAD(opA, opB, sum2);
+ opA = __SXTB16(__ROR(inA1, 8));
+ opB = __SXTB16(__ROR(inB1, 8));
+ sum = __SMLAD(opA, opB, sum);
+ opA = __SXTB16(inA2);
+ opB = __SXTB16(inB2);
+ sum4 = __SMLAD(opA, opB, sum4);
+ opA = __SXTB16(__ROR(inA2, 8));
+ opB = __SXTB16(__ROR(inB2, 8));
+ sum3 = __SMLAD(opA, opB, sum3);
+ colCnt--;
+ }
+
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ // r0 r1 r2 r3 r4 r5
+ // inA1, inA2, inB1, inB2, opA, opB
+ asm volatile ("COL_LOOP:\n"
+ "ldr.w r2, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "ldr.w r5, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "pkhtb r3, r5, r2, ASR #16\n"
+ "pkhbt r2, r2, r5, LSL #16\n"
+ "ldr.w r0, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "ldr.w r5, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "pkhtb r1, r5, r0, ASR #16\n"
+ "pkhbt r0, r0, r5, LSL #16\n"
+ "sxtb16 r4, r0\n"
+ "sxtb16 r5, r2\n"
+ "smlad %[sum], r4, r5, %[sum]\n"
+ "mov.w r4, r0, ror #8\n"
+ "mov.w r5, r2, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum2], r4, r5, %[sum2]\n"
+ "sxtb16 r4, r1\n"
+ "sxtb16 r5, r3\n"
+ "smlad %[sum3], r4, r5, %[sum3]\n"
+ "mov.w r4, r1, ror #8\n"
+ "mov.w r5, r3, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum4], r4, r5, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+ [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+ // r0 r1 r2 r3 r4 r5
+ // inA1, inA2, inB1, inB2, opA, opB
+ asm volatile ("COL_LOOP:\n"
+ "ldr.w r2, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "ldr.w r5, [%[pB], #0]\n"
+ "add.w %[pB], %[pB], %[ch_im_in]\n"
+ "pkhbt r3, r5, r2, LSL #16\n"
+ "pkhtb r2, r2, r5, ASR #16\n"
+ "ldr.w r0, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "ldr.w r5, [%[pA], #0]\n"
+ "add.w %[pA], %[pA], %[ch_im_in]\n"
+ "pkhbt r1, r5, r0, LSL #16\n"
+ "pkhtb r0, r0, r5, ASR #16\n"
+ "sxtb16 r4, r0\n"
+ "sxtb16 r5, r2\n"
+ "smlad %[sum2], r4, r5, %[sum2]\n"
+ "mov.w r4, r0, ror #8\n"
+ "mov.w r5, r2, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum], r4, r5, %[sum]\n"
+ "sxtb16 r4, r1\n"
+ "sxtb16 r5, r3\n"
+ "smlad %[sum4], r4, r5, %[sum4]\n"
+ "mov.w r4, r1, ror #8\n"
+ "mov.w r5, r3, ror #8\n"
+ "sxtb16 r4, r4\n"
+ "sxtb16 r5, r5\n"
+ "smlad %[sum3], r4, r5, %[sum3]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+ [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#endif /*ARM_MATH_BIG_ENDIAN */
+
+#endif /* USE_INTRINSIC */
+
+ colCnt = (dim_kernel_x * dim_kernel_y) & 0x1;
+ while (colCnt)
+ {
+ union arm_nnword inA, inB;
+ inA.word = *__SIMD32(pA);
+ pA += ch_im_in;
+ inB.word = *__SIMD32(pB);
+ pB += ch_im_in;
+ sum += inA.bytes[0] * inB.bytes[0];
+ sum2 += inA.bytes[1] * inB.bytes[1];
+ sum3 += inA.bytes[2] * inB.bytes[2];
+ sum4 += inA.bytes[3] * inB.bytes[3];
+ colCnt--;
+ }
+
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ rowCnt--;
+ }
+
+ rowCnt = ch_im_out & 0x3;
+ while (rowCnt)
+ {
+ q7_t *pB = colBuffer + row_shift;
+ const q7_t *pA = wt + row_shift;
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = (dim_kernel_x * dim_kernel_y);
+
+ row_shift += 1;
+
+ while (colCnt)
+ {
+ q7_t A1 = *pA;
+ q7_t B1 = *pB;
+ pA += ch_im_in;
+ pB += ch_im_in;
+ sum += A1 * B1;
+
+ colCnt--;
+ }
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ rowCnt--;
+ }
+
+ // clear counter and pointers
+ pBuffer = colBuffer;
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ int i_out_y, i_out_x, i_ch_out;
+ int i_ker_y, i_ker_x;
+
+ /* do some checking here, basically ch_im_in == ch_im_out */
+ if (ch_im_in != ch_im_out)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+ {
+ for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+ {
+ for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+ {
+ // for each output
+ int conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+ for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
+ {
+ for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
+ {
+ int in_row = stride_y * i_out_y + i_ker_y - padding_y;
+ int in_col = stride_x * i_out_x + i_ker_x - padding_x;
+ if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+ {
+ conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *
+ wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
+ }
+ }
+ }
+ Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
+ (q7_t) __SSAT((conv_out >> out_shift), 8);
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+
+ /* Return to application */
+ return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c new file mode 100644 index 0000000..24ab412 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c @@ -0,0 +1,187 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_mat_mult_kernel_q7_q15.c
+ * Description: Matrix-multiplication function for convolution
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+ /**
+ * @brief Matrix-multiplication function for convolution
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ *
+ * @details
+ *
+ * This function does the matrix multiplication with weight matrix
+ * and 2 columns from im2col.
+ */
+
+q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut)
+{
+#if defined (ARM_MATH_DSP)
+ /* set up the second output pointers */
+ q7_t *pOut2 = pOut + ch_im_out;
+ const q7_t *pBias = bias;
+
+ uint16_t rowCnt = ch_im_out >> 1;
+ /* this loop over rows in A */
+ while (rowCnt)
+ {
+ /* setup pointers for B */
+ const q15_t *pB = pInBuffer;
+ const q15_t *pB2 = pB + numCol_A;
+
+ /* align the second pointer for A */
+ const q7_t *pA2 = pA + numCol_A;
+
+ /* init the sum with bias */
+ q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = numCol_A >> 2;
+ /* accumulate over the vector */
+ while (colCnt)
+ {
+ q31_t inA11, inA12, inA21, inA22;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inB2 = *__SIMD32(pB2)++;
+
+ pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+ pA2 = (q7_t *) read_and_pad((void *)pA2, &inA21, &inA22);
+
+ sum = __SMLAD(inA11, inB1, sum);
+ sum2 = __SMLAD(inA11, inB2, sum2);
+ sum3 = __SMLAD(inA21, inB1, sum3);
+ sum4 = __SMLAD(inA21, inB2, sum4);
+
+ inB1 = *__SIMD32(pB)++;
+ inB2 = *__SIMD32(pB2)++;
+
+ sum = __SMLAD(inA12, inB1, sum);
+ sum2 = __SMLAD(inA12, inB2, sum2);
+ sum3 = __SMLAD(inA22, inB1, sum3);
+ sum4 = __SMLAD(inA22, inB2, sum4);
+
+ colCnt--;
+ } /* while over colCnt */
+ colCnt = numCol_A & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ q7_t inA2 = *pA2++;
+ q15_t inB2 = *pB2++;
+
+ sum += inA1 * inB1;
+ sum2 += inA1 * inB2;
+ sum3 += inA2 * inB1;
+ sum4 += inA2 * inB2;
+ colCnt--;
+ } /* while over colCnt */
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ /* skip the row computed with A2 */
+ pA += numCol_A;
+ rowCnt--;
+ } /* for over ch_im_out */
+
+ /* compute left-over row if any */
+ if (ch_im_out & 0x1)
+ {
+ /* setup pointers for B */
+ const q15_t *pB = pInBuffer;
+ const q15_t *pB2 = pB + numCol_A;
+
+ /* load the bias */
+ q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = numCol_A >> 2;
+ while (colCnt)
+ {
+ q31_t inA11, inA12;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inB2 = *__SIMD32(pB2)++;
+
+ pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+
+ sum = __SMLAD(inA11, inB1, sum);
+ sum2 = __SMLAD(inA11, inB2, sum2);
+
+ inB1 = *__SIMD32(pB)++;
+ inB2 = *__SIMD32(pB2)++;
+ sum = __SMLAD(inA12, inB1, sum);
+ sum2 = __SMLAD(inA12, inB2, sum2);
+
+ colCnt--;
+ }
+ colCnt = numCol_A & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ q15_t inB2 = *pB2++;
+
+ sum += inA1 * inB1;
+ sum2 += inA1 * inB2;
+ colCnt--;
+ }
+
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ }
+
+ pOut += ch_im_out;
+
+ /* return the new output pointer with offset */
+ return pOut;
+#else
+ /* To be completed */
+ return NULL;
+#endif /* ARM_MATH_DSP */
+
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c new file mode 100644 index 0000000..36af21a --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c @@ -0,0 +1,138 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_mat_mult_kernel_q7_q15_reordered.c
+ * Description: Matrix-multiplication function for convolution with reordered columns
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+#include "arm_math.h"
+
+ /**
+ * @brief Matrix-multiplication function for convolution with reordered columns
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ *
+ * @details
+ *
+ * This function assumes that data in pInBuffer are reordered
+ */
+
+q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* set up the second output pointers */
+ q7_t *pOut2 = pOut + ch_im_out;
+ int i;
+
+ /* this loop over rows in A */
+ for (i = 0; i < ch_im_out; i += 2)
+ {
+ /* setup pointers for B */
+ const q15_t *pB = pInBuffer;
+ const q15_t *pB2 = pB + numCol_A;
+
+ /* align the second pointer for A */
+ const q7_t *pA2 = pA + numCol_A;
+
+ /* init the sum with bias */
+ q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = numCol_A >> 2;
+ /* accumulate over the vector */
+ while (colCnt)
+ {
+ q31_t inA11, inA12, inA21, inA22;
+ q31_t inB1 = *__SIMD32(pB)++;
+ q31_t inB2 = *__SIMD32(pB2)++;
+
+ pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA11, &inA12);
+ pA2 = (q7_t *) read_and_pad_reordered((void *)pA2, &inA21, &inA22);
+
+ sum = __SMLAD(inA11, inB1, sum);
+ sum2 = __SMLAD(inA11, inB2, sum2);
+ sum3 = __SMLAD(inA21, inB1, sum3);
+ sum4 = __SMLAD(inA21, inB2, sum4);
+
+ inB1 = *__SIMD32(pB)++;
+ inB2 = *__SIMD32(pB2)++;
+
+ sum = __SMLAD(inA12, inB1, sum);
+ sum2 = __SMLAD(inA12, inB2, sum2);
+ sum3 = __SMLAD(inA22, inB1, sum3);
+ sum4 = __SMLAD(inA22, inB2, sum4);
+
+ colCnt--;
+ } /* while over colCnt */
+ colCnt = numCol_A & 0x3;
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q15_t inB1 = *pB++;
+ q7_t inA2 = *pA2++;
+ q15_t inB2 = *pB2++;
+
+ sum += inA1 * inB1;
+ sum2 += inA1 * inB2;
+ sum3 += inA2 * inB1;
+ sum4 += inA2 * inB2;
+ colCnt--;
+ } /* while over colCnt */
+ *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ /* skip the row computed with A2 */
+ pA += numCol_A;
+ } /* for over ch_im_out */
+
+ pOut += ch_im_out;
+
+ /* return the new output pointer with offset */
+ return pOut;
+#else
+ /* To be completed */
+ return NULL;
+#endif /* ARM_MATH_DSP */
+}
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c new file mode 100644 index 0000000..bb9a091 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c @@ -0,0 +1,199 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_mat_q7_vec_q15.c
+ * Description: Mixed Q15-Q7 fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Mixed Q15-Q7 fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: 0
+ *
+ * Q7_Q15 version of the fully connected layer
+ *
+ * Weights are in q7_t and Activations are in q15_t
+ *
+ */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q7_t *pB = pM;
+ const q7_t *pB2;
+ q15_t *pO = pOut;
+ const q7_t *pBias = bias;
+ const q15_t *pA = pV;
+
+ uint16_t rowCnt = num_of_rows >> 1;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+ pB2 = pB + dim_vec;
+
+ while (colCnt)
+ {
+ q31_t inV, inM11, inM12, inM21, inM22;
+ pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+ pB2 = (q7_t *) read_and_pad((void *)pB2, &inM21, &inM22);
+
+ inV = *__SIMD32(pA)++;
+
+ sum = __SMLAD(inV, inM11, sum);
+ sum2 = __SMLAD(inV, inM21, sum2);
+
+ inV = *__SIMD32(pA)++;
+
+ sum = __SMLAD(inV, inM12, sum);
+ sum2 = __SMLAD(inV, inM22, sum2);
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ q7_t inM2 = *pB2++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+
+ /*adjust the pointers and counters */
+ pB += dim_vec;
+ rowCnt--;
+ }
+
+ /* left-over part of the rows */
+ rowCnt = num_of_rows & 0x1;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+
+ while (colCnt)
+ {
+ q31_t inV1, inV2, inM11, inM12;
+
+ pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+ inV1 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV1, inM11, sum);
+
+ inV2 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV2, inM12, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ sum += inV * inM;
+ colCnt--;
+ }
+
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+ rowCnt--;
+ }
+
+#else
+ int i, j;
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ for (i = 0; i < num_of_rows; i++)
+ {
+ int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ for (j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to ARM_MATH_SUCCESS */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c new file mode 100644 index 0000000..b0c308b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c @@ -0,0 +1,403 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_mat_q7_vec_q15_opt.c
+ * Description: Mixed Q15-Q7 opt fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Mixed Q15-Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: 0
+ *
+ * Q7_Q15 version of the fully connected layer
+ *
+ * Weights are in q7_t and Activations are in q15_t
+ *
+ * Limitation: x4 version requires weight reordering to work
+ *
+ * Here we use only one pointer to read 4 rows in the weight
+ * matrix. So if the original q7_t matrix looks like this:
+ *
+ * | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+ *
+ * | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+ *
+ * | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+ *
+ * | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+ *
+ * | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+ *
+ * | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+ *
+ * We operates on multiple-of-4 rows, so the first four rows becomes
+ *
+ * | a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 |
+ *
+ * | a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 |
+ *
+ * | a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 |
+ *
+ * The column left over will be in-order.
+ * which is:
+ * | a17 | a27 | a37 | a47 |
+ *
+ * For the left-over rows, we do 1x1 computation, so the data remains
+ * as its original order.
+ *
+ * So the stored weight matrix looks like this:
+ *
+ * | a11 | a21 | a12 | a22 | a31 | a41 |
+ *
+ * | a32 | a42 | a13 | a23 | a14 | a24 |
+ *
+ * | a33 | a43 | a34 | a44 | a15 | a25 |
+ *
+ * | a16 | a26 | a35 | a45 | a36 | a46 |
+ *
+ * | a17 | a27 | a37 | a47 | a51 | a52 |
+ *
+ * | a53 | a54 | a55 | a56 | a57 | a61 |
+ *
+ * | a62 | a63 | a64 | a65 | a66 | a67 |
+ *
+ */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift, const q7_t * bias, q15_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q7_t *pB = pM;
+ q15_t *pO = pOut;
+ const q7_t *pBias = bias;
+ const q15_t *pA = pV;
+
+ uint16_t rowCnt = num_of_rows >> 2;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 1;
+
+ pA = pV;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ while (colCnt)
+ {
+ q31_t inM11, inM12, inM13, inM14;
+ q31_t inV;
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM11, inV, sum);
+ sum2 = __SMLAD(inM12, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM13, inV, sum3);
+ sum4 = __SMLAD(inM14, inV, sum4);
+ colCnt--;
+ }
+
+#else
+
+ while (colCnt)
+ {
+ q31_t inM11, inM12, inM13, inM14;
+ q31_t inV;
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM12, inV, sum);
+ sum2 = __SMLAD(inM11, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM14, inV, sum3);
+ sum4 = __SMLAD(inM13, inV, sum4);
+ colCnt--;
+ }
+
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+ /*
+ * register needed:
+ * loop counter: colCnt
+ * accumulators: sum, sum2, sum3, sum4
+ * pointers: pB, pA
+ * weight data: inM11, inM12, inM13, inM14
+ * activation data: inV
+ */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r4, [%[pA]], #4\n"
+ "ldr.w r1, [%[pB]], #8\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r1, %[sum]\n"
+ "smlad %[sum2], r4, r0, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-4]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r3, %[sum3]\n"
+ "smlad %[sum4], r4, r2, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+ [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r4, [%[pA]], #4\n"
+ "ldr.w r1, [%[pB]], #8\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r0, %[sum]\n"
+ "smlad %[sum2], r4, r1, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-4]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r2, %[sum3]\n"
+ "smlad %[sum4], r4, r3, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+ [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#endif /* USE_INTRINSIC */
+
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ q7_t inM2 = *pB++;
+ q7_t inM3 = *pB++;
+ q7_t inM4 = *pB++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ sum3 += inV * inM3;
+ sum4 += inV * inM4;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+ /* adjust the pointers and counters */
+ rowCnt--;
+ }
+
+ /* left-over part of the rows */
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+
+ while (colCnt)
+ {
+ q31_t inV1, inV2, inM11, inM12;
+
+ pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+ inV1 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV1, inM11, sum);
+
+ inV2 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV2, inM12, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ sum += inV * inM;
+ colCnt--;
+ }
+
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+ rowCnt--;
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q7_t *pB = pM;
+ const q15_t *pA;
+ q15_t *pO = pOut;
+ const q7_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = dim_vec >> 1;
+
+ pA = pV;
+
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inA2 = *pA++;
+
+ q7_t inB1 = *pB++;
+ q7_t inB3 = *pB++;
+ q7_t inB2 = *pB++;
+ q7_t inB4 = *pB++;
+
+ sum += inA1 * inB1 + inA2 * inB2;
+ sum2 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA1 * inB1 + inA2 * inB2;
+ sum4 += inA1 * inB3 + inA2 * inB4;
+
+ colCnt--;
+ }
+
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+ q15_t inA = *pA++;
+ q7_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+
+ colCnt--;
+ }
+ *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+ rowCnt--;
+ }
+
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ int j;
+
+ pA = pV;
+ for (j = 0; j < dim_vec; j++)
+ {
+ q15_t inA = *pA++;
+ q7_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+ rowCnt--;
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to ARM_MATH_SUCCESS */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c new file mode 100644 index 0000000..a4c6bba --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c @@ -0,0 +1,193 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_q15.c
+ * Description: Q15 basic fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Q15 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: 0
+ *
+ */
+
+arm_status
+arm_fully_connected_q15(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q15_t *pB = pM;
+ const q15_t *pB2 = pB + dim_vec;
+ q15_t *pO = pOut;
+ const q15_t *pA;
+ const q15_t *pBias = bias;
+ uint16_t rowCnt = num_of_rows >> 1;
+
+ /* this loop loops over different output */
+ while (rowCnt) {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+ pB2 = pB + dim_vec;
+
+ while (colCnt)
+ {
+ q31_t inV1, inM1, inM2;
+ inV1 = *__SIMD32(pA)++;
+ inM1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inV1, inM1, sum);
+ inM2 = *__SIMD32(pB2)++;
+ sum2 = __SMLAD(inV1, inM2, sum2);
+
+ inV1 = *__SIMD32(pA)++;
+ inM1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inV1, inM1, sum);
+ inM2 = *__SIMD32(pB2)++;
+ sum2 = __SMLAD(inV1, inM2, sum2);
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q15_t inM = *pB++;
+ q15_t inM2 = *pB2++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum2>> out_shift), 16));
+
+ /* adjust the pointers and counters */
+ pB = pB + dim_vec;
+ rowCnt --;
+ }
+
+ rowCnt = num_of_rows & 0x1;
+
+ while (rowCnt) {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+
+ while (colCnt) {
+ q31_t inV1, inM1;
+ inV1 = *__SIMD32(pA)++;
+ inM1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inV1, inM1, sum);
+
+ inV1 = *__SIMD32(pA)++;
+ inM1 = *__SIMD32(pB)++;
+ sum = __SMLAD(inV1, inM1, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while(colCnt) {
+ q15_t inV = *pA++;
+ q15_t inM = *pB++;
+
+ sum += inV * inM;
+
+ colCnt--;
+ }
+
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+ rowCnt --;
+ }
+
+#else
+ int i, j;
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ for (i = 0; i < num_of_rows; i++)
+ {
+ int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ for (j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to application */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c new file mode 100644 index 0000000..8f3bbea --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c @@ -0,0 +1,332 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_q15_opt.c
+ * Description: Q15 opt fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Q15 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: 0
+ *
+ * Here we use only one pointer to read 4 rows in the weight
+ * matrix. So if the original matrix looks like this:
+ *
+ * | a11 | a12 | a13 |
+ *
+ * | a21 | a22 | a23 |
+ *
+ * | a31 | a32 | a33 |
+ *
+ * | a41 | a42 | a43 |
+ *
+ * | a51 | a52 | a53 |
+ *
+ * | a61 | a62 | a63 |
+ *
+ * We operates on multiple-of-4 rows, so the first four rows becomes
+ *
+ * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+ *
+ * | a13 | a23 | a33 | a43 |
+ *
+ * Remaining rows are kept the same original order.
+ *
+ * So the stored weight matrix looks like this:
+ *
+ *
+ * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+ *
+ * | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 |
+ *
+ * | a62 | a63 |
+ */
+
+arm_status
+arm_fully_connected_q15_opt(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q15_t *pB = pM;
+ q15_t *pO = pOut;
+ const q15_t *pBias = bias;
+ const q15_t *pA = pV;
+
+ uint16_t rowCnt = num_of_rows >> 2;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 1;
+
+ pA = pV;
+
+#ifdef USE_INTRINSIC
+
+ while (colCnt)
+ {
+ q31_t inM11, inM12, inM13, inM14;
+ q31_t inV;
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ sum = __SMLAD(inV, inM11, sum);
+ inM12 = *__SIMD32(pB)++;
+ sum2 = __SMLAD(inV, inM12, sum2);
+ inM13 = *__SIMD32(pB)++;
+ sum3 = __SMLAD(inV, inM13, sum3);
+ inM14 = *__SIMD32(pB)++;
+ sum4 = __SMLAD(inV, inM14, sum4);
+ colCnt--;
+ }
+
+#else
+
+ /*
+ * register needed:
+ * loop counter: colCnt
+ * accumulators: sum, sum2, sum3, sum4
+ * pointers: pB, pA
+ * weight data: inM11, inM12, inM13, inM14
+ * activation data: inV
+ */
+
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r4, [%[pA]], #4\n"
+ "ldr.w r0, [%[pB]], #16\n"
+ "smlad %[sum], r4, r0, %[sum]\n"
+ "ldr.w r1, [%[pB] , #-12]\n"
+ "smlad %[sum2], r4, r1, %[sum2]\n"
+ "ldr.w r2, [%[pB] , #-8]\n"
+ "smlad %[sum3], r4, r2, %[sum3]\n"
+ "ldr.w r3, [%[pB] , #-4]\n"
+ "smlad %[sum4], r4, r3, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+ [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+
+#endif /* USE_INTRINSIC */
+
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+
+ q15_t inV = *pA++;
+ q15_t inM = *pB++;
+ q15_t inM2 = *pB++;
+ q15_t inM3 = *pB++;
+ q15_t inM4 = *pB++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ sum3 += inV * inM3;
+ sum4 += inV * inM4;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+ *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+ /* adjust the pointers and counters */
+ rowCnt--;
+ }
+
+ /* left-over part of the rows */
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+
+ while (colCnt)
+ {
+ q31_t inV1, inV2, inM1, inM2;
+
+ inM1 = *__SIMD32(pB)++;
+ inV1 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV1, inM1, sum);
+
+ inM2 = *__SIMD32(pB)++;
+ inV2 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV2, inM2, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q15_t inM = *pB++;
+ sum += inV * inM;
+ colCnt--;
+ }
+
+ *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+ rowCnt--;
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q15_t *pB = pM;
+ const q15_t *pA;
+ q15_t *pO = pOut;
+ const q15_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 1;
+
+ pA = pV;
+ while (colCnt)
+ {
+ q15_t inA1 = *pA++;
+ q15_t inA2 = *pA++;
+
+ q15_t inB1 = *pB++;
+ q15_t inB2 = *pB++;
+ sum += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum2 += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum3 += inA1 * inB1 + inA2 * inB2;
+
+ inB1 = *pB++;
+ inB2 = *pB++;
+ sum4 += inA1 * inB1 + inA2 * inB2;
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x1;
+ while (colCnt)
+ {
+ q15_t inA = *pA++;
+ q15_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+ colCnt--;
+ }
+ *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+ *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+ rowCnt--;
+ }
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ int j;
+
+ pA = pV;
+ for (j = 0; j < dim_vec; j++)
+ {
+ q15_t inA = *pA++;
+ q15_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+ rowCnt--;
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to ARM_MATH_SUCCESS */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c new file mode 100644 index 0000000..75e924f --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c @@ -0,0 +1,198 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_q7.c
+ * Description: Q7 basic fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Q7 basic fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: dim_vec
+ *
+ * This basic function is designed to work with regular weight
+ * matrix without interleaving.
+ *
+ */
+
+arm_status
+arm_fully_connected_q7(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift, const q7_t * bias, q7_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q7_t *pB = pM;
+ const q7_t *pB2;
+ q7_t *pO = pOut;
+ const q7_t *pBias = bias;
+ q15_t *pA;
+ uint16_t rowCnt = num_of_rows >> 1;
+
+ /* expand the vector into the buffer */
+ arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = vec_buffer;
+ pB2 = pB + dim_vec;
+
+ while (colCnt)
+ {
+ q31_t inV, inM11, inM12, inM21, inM22;
+ pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+ pB2 = (q7_t *) read_and_pad_reordered((void *)pB2, &inM21, &inM22);
+
+ inV = *__SIMD32(pA)++;
+
+ sum = __SMLAD(inV, inM11, sum);
+ sum2 = __SMLAD(inV, inM21, sum2);
+
+ inV = *__SIMD32(pA)++;
+
+ sum = __SMLAD(inV, inM12, sum);
+ sum2 = __SMLAD(inV, inM22, sum2);
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q7_t inV = *pA++;
+ q15_t inM = *pB++;
+ q15_t inM2 = *pB2++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+ *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+
+ /* adjust the pointers and counters */
+ pB += dim_vec;
+ rowCnt--;
+ }
+
+ /* left-over part of the rows */
+ rowCnt = num_of_rows & 0x1;
+
+ while (rowCnt)
+ {
+ uint16_t colCnt = dim_vec >> 2;
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ pA = vec_buffer;
+
+ while (colCnt)
+ {
+ q31_t inV1, inV2, inM11, inM12;
+
+ pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+ inV1 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV1, inM11, sum);
+
+ inV2 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV2, inM12, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q7_t inV = *pA++;
+ q15_t inM = *pB++;
+ sum += inV * inM;
+ colCnt--;
+ }
+
+ *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+ rowCnt--;
+ }
+
+#else
+ int i, j;
+
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ for (i = 0; i < num_of_rows; i++)
+ {
+ int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+ for (j = 0; j < dim_vec; j++)
+ {
+ ip_out += pV[j] * pM[i * dim_vec + j];
+ }
+ pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8);
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to ARM_MATH_SUCCESS */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c new file mode 100644 index 0000000..d197adc --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c @@ -0,0 +1,484 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_fully_connected_q7_opt.c
+ * Description: Q7 basic fully-connected layer function
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+ /**
+ * @brief Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * vec_buffer size: dim_vec
+ *
+ * This opt function is designed to work with interleaved weight
+ * matrix. The vector input is assumed in q7_t format, we call
+ * arm_q7_to_q15_no_shift_shuffle function to expand into
+ * q15_t format with certain weight re-ordering, refer to the function
+ * comments for more details.
+ * Here we use only one pointer to read 4 rows in the weight
+ * matrix. So if the original q7_t matrix looks like this:
+ *
+ * | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+ *
+ * | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+ *
+ * | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+ *
+ * | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+ *
+ * | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+ *
+ * | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+ *
+ *
+ * We operates on multiple-of-4 rows, so the first four rows becomes
+ *
+ * | a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 |
+ *
+ * | a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 |
+ *
+ * | a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 |
+ *
+ * So within the kernel, we first read the re-ordered vector in as:
+ *
+ * | b1 | b3 | and | b2 | b4 |
+ *
+ * the four q31_t weights will look like
+ *
+ * | a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 |
+ *
+ * | a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 |
+ *
+ * The column left over will be in-order.
+ * which is:
+ *
+ * | a17 | a27 | a37 | a47 |
+ *
+ * For the left-over rows, we do 1x1 computation, so the data remains
+ * as its original order.
+ *
+ * So the stored weight matrix looks like this:
+ *
+ * | a11 | a21 | a13 | a23 | a31 | a41 |
+ *
+ * | a33 | a43 | a12 | a22 | a14 | a24 |
+ *
+ * | a32 | a42 | a34 | a44 | a15 | a25 |
+ *
+ * | a35 | a45 | a16 | a26 | a36 | a46 |
+ *
+ * | a17 | a27 | a37 | a47 | a51 | a52 |
+ *
+ * | a53 | a54 | a55 | a56 | a57 | a61 |
+ *
+ * | a62 | a63 | a64 | a65 | a66 | a67 |
+ *
+ *
+ */
+
+arm_status
+arm_fully_connected_q7_opt(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut,
+ q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ const q7_t *pB = pM;
+ q7_t *pO = pOut;
+ const q7_t *pBias = bias;
+ q15_t *pA;
+ uint16_t rowCnt = num_of_rows >> 2;
+
+ arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+ while (rowCnt)
+ {
+
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = vec_buffer;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ while (colCnt)
+ {
+ q31_t inM11, inM12, inM13, inM14;
+ q31_t inV;
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM11, inV, sum);
+ sum2 = __SMLAD(inM12, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM13, inV, sum3);
+ sum4 = __SMLAD(inM14, inV, sum4);
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM11, inV, sum);
+ sum2 = __SMLAD(inM12, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM13, inV, sum3);
+ sum4 = __SMLAD(inM14, inV, sum4);
+ colCnt--;
+ }
+#else
+ while (colCnt)
+ {
+ q31_t inM11, inM12, inM13, inM14;
+ q31_t inV;
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM12, inV, sum);
+ sum2 = __SMLAD(inM11, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM14, inV, sum3);
+ sum4 = __SMLAD(inM13, inV, sum4);
+
+ inV = *__SIMD32(pA)++;
+ inM11 = *__SIMD32(pB)++;
+ inM12 = __SXTB16(__ROR(inM11, 8));
+ inM11 = __SXTB16(inM11);
+ sum = __SMLAD(inM12, inV, sum);
+ sum2 = __SMLAD(inM11, inV, sum2);
+ inM13 = *__SIMD32(pB)++;
+ inM14 = __SXTB16(__ROR(inM13, 8));
+ inM13 = __SXTB16(inM13);
+ sum3 = __SMLAD(inM14, inV, sum3);
+ sum4 = __SMLAD(inM13, inV, sum4);
+ colCnt--;
+ }
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+ /*
+ * register needed:
+ * loop counter: colCnt
+ * accumulators: sum, sum2, sum3, sum4
+ * pointers: pB, pA
+ * weight data: inM11, inM12, inM13, inM14
+ * activation data: inV
+ */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r4, [%[pA]], #8\n"
+ "ldr.w r1, [%[pB]], #16\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r1, %[sum]\n"
+ "smlad %[sum2], r4, r0, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-12]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r3, %[sum3]\n"
+ "smlad %[sum4], r4, r2, %[sum4]\n"
+ "ldr.w r4, [%[pA], #-4]\n"
+ "ldr.w r1, [%[pB], #-8]\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r1, %[sum]\n"
+ "smlad %[sum2], r4, r0, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-4]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r3, %[sum3]\n"
+ "smlad %[sum4], r4, r2, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+ [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+ asm volatile ("COL_LOOP_%=:\n"
+ "ldr.w r4, [%[pA]], #8\n"
+ "ldr.w r1, [%[pB]], #16\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r0, %[sum]\n"
+ "smlad %[sum2], r4, r1, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-12]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r2, %[sum3]\n"
+ "smlad %[sum4], r4, r3, %[sum4]\n"
+ "ldr.w r4, [%[pA], #-4]\n"
+ "ldr.w r1, [%[pB], #-8]\n"
+ "mov.w r0, r1, ror #8\n"
+ "sxtb16 r0, r0\n"
+ "sxtb16 r1, r1\n"
+ "smlad %[sum], r4, r0, %[sum]\n"
+ "smlad %[sum2], r4, r1, %[sum2]\n"
+ "ldr.w r3, [%[pB], #-4]\n"
+ "mov.w r2, r3, ror #8\n"
+ "sxtb16 r2, r2\n"
+ "sxtb16 r3, r3\n"
+ "smlad %[sum3], r4, r2, %[sum3]\n"
+ "smlad %[sum4], r4, r3, %[sum4]\n"
+ "subs %[colCnt], #1\n"
+ "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+ [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+ [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif /* ARM_MATH_BIG_ENDIAN */
+
+#endif /* USE_INTRINSIC */
+
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ q7_t inM2 = *pB++;
+ q7_t inM3 = *pB++;
+ q7_t inM4 = *pB++;
+
+ sum += inV * inM;
+ sum2 += inV * inM2;
+ sum3 += inV * inM3;
+ sum4 += inV * inM4;
+ colCnt--;
+ } /* while over colCnt */
+ *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+ *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+ *pO++ = (q7_t) (__SSAT((sum3 >> out_shift), 8));
+ *pO++ = (q7_t) (__SSAT((sum4 >> out_shift), 8));
+
+ /* adjust the pointers and counters */
+ rowCnt--;
+ }
+
+ /* left-over part of the rows */
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = vec_buffer;
+
+ while (colCnt)
+ {
+ q31_t inV1, inV2, inM11, inM12;
+
+ pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+ inV1 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV1, inM11, sum);
+
+ inV2 = *__SIMD32(pA)++;
+ sum = __SMLAD(inV2, inM12, sum);
+
+ colCnt--;
+ }
+
+ /* left-over of the vector */
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q15_t inV = *pA++;
+ q7_t inM = *pB++;
+ sum += inV * inM;
+ colCnt--;
+ }
+
+ *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+ rowCnt--;
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+ uint16_t rowCnt = num_of_rows >> 2;
+ const q7_t *pB = pM;
+ const q7_t *pA;
+ q7_t *pO = pOut;
+ const q7_t *pBias = bias;
+
+ while (rowCnt)
+ {
+ q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+ q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ uint16_t colCnt = dim_vec >> 2;
+
+ pA = pV;
+
+ while (colCnt)
+ {
+ q7_t inA1 = *pA++;
+ q7_t inA3 = *pA++;
+ q7_t inA2 = *pA++;
+ q7_t inA4 = *pA++;
+
+ q7_t inB1 = *pB++;
+ q7_t inB3 = *pB++;
+ q7_t inB2 = *pB++;
+ q7_t inB4 = *pB++;
+
+ sum += inA1 * inB1 + inA2 * inB2;
+ sum2 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA1 * inB1 + inA2 * inB2;
+ sum4 += inA1 * inB3 + inA2 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum += inA3 * inB1 + inA4 * inB2;
+ sum2 += inA3 * inB3 + inA4 * inB4;
+
+ inB1 = *pB++;
+ inB3 = *pB++;
+ inB2 = *pB++;
+ inB4 = *pB++;
+
+ sum3 += inA3 * inB1 + inA4 * inB2;
+ sum4 += inA3 * inB3 + inA4 * inB4;
+
+ colCnt--;
+ }
+ colCnt = dim_vec & 0x3;
+ while (colCnt)
+ {
+ q7_t inA = *pA++;
+ q7_t inB = *pB++;
+ sum += inA * inB;
+ inB = *pB++;
+ sum2 += inA * inB;
+ inB = *pB++;
+ sum3 += inA * inB;
+ inB = *pB++;
+ sum4 += inA * inB;
+
+ colCnt--;
+ }
+ *pO++ = (q7_t) __SSAT((sum >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+ *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+ rowCnt--;
+ }
+
+ rowCnt = num_of_rows & 0x3;
+
+ while (rowCnt)
+ {
+ int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+ int j;
+
+ pA = pV;
+ for (j = 0; j < dim_vec; j++)
+ {
+ q7_t inA = *pA++;
+ q7_t inB = *pB++;
+ ip_out += inA * inB;
+ }
+ *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8);
+
+ rowCnt--;
+ }
+
+#endif /* ARM_MATH_DSP */
+
+ /* Return to ARM_MATH_SUCCESS */
+ return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c new file mode 100644 index 0000000..de7668b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c @@ -0,0 +1,147 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_mult_q15.c
+ * Description: Q15 vector multiplication with variable output shifts
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+ q15_t * pSrcA,
+ q15_t * pSrcB,
+ q15_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize)
+{
+ uint32_t blkCnt; /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+ q31_t inA1, inA2, inB1, inB2; /* temporary input variables */
+ q15_t out1, out2, out3, out4; /* temporary output variables */
+ q31_t mul1, mul2, mul3, mul4; /* temporary variables */
+
+ /* loop Unrolling */
+ blkCnt = blockSize >> 2U;
+
+ /* First part of the processing with loop unrolling. Compute 4 outputs at a time.
+ ** a second loop below computes the remaining 1 to 3 samples. */
+ while (blkCnt > 0U)
+ {
+ /* read two samples at a time from sourceA */
+ inA1 = *__SIMD32(pSrcA)++;
+ /* read two samples at a time from sourceB */
+ inB1 = *__SIMD32(pSrcB)++;
+ /* read two samples at a time from sourceA */
+ inA2 = *__SIMD32(pSrcA)++;
+ /* read two samples at a time from sourceB */
+ inB2 = *__SIMD32(pSrcB)++;
+
+ /* multiply mul = sourceA * sourceB */
+ mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16));
+ mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1);
+ mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16));
+ mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2);
+
+ /* saturate result to 16 bit */
+ out1 = (q15_t) __SSAT((mul1 + NN_ROUND(out_shift)) >> out_shift, 16);
+ out2 = (q15_t) __SSAT((mul2 + NN_ROUND(out_shift)) >> out_shift, 16);
+ out3 = (q15_t) __SSAT((mul3 + NN_ROUND(out_shift)) >> out_shift, 16);
+ out4 = (q15_t) __SSAT((mul4 + NN_ROUND(out_shift)) >> out_shift, 16);
+
+ /* store the result */
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+ *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#else
+
+ *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+ *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#endif /* #ifndef ARM_MATH_BIG_ENDIAN */
+
+ /* Decrement the blockSize loop counter */
+ blkCnt--;
+ }
+
+ /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+ ** No loop unrolling is used. */
+ blkCnt = blockSize % 0x4U;
+
+#else
+
+ /* Run the below code for Cortex-M0 */
+
+ /* Initialize blkCnt with number of samples */
+ blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+ while (blkCnt > 0U)
+ {
+ /* C = A * B */
+ /* Multiply the inputs and store the result in the destination buffer */
+ *pDst++ = (q15_t) __SSAT((((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16);
+
+ /* Decrement the blockSize loop counter */
+ blkCnt--;
+ }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */
+
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c new file mode 100644 index 0000000..1b4e02c --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c @@ -0,0 +1,119 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_mult_q7.c
+ * Description: Q7 vector multiplication with variable output shifts
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+ q7_t * pSrcA,
+ q7_t * pSrcB,
+ q7_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize)
+{
+ uint32_t blkCnt; /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+ q7_t out1, out2, out3, out4; /* Temporary variables to store the product */
+
+ /* loop Unrolling */
+ blkCnt = blockSize >> 2U;
+
+ /* First part of the processing with loop unrolling. Compute 4 outputs at a time.
+ ** a second loop below computes the remaining 1 to 3 samples. */
+ while (blkCnt > 0U)
+ {
+ /* C = A * B */
+ /* Multiply the inputs and store the results in temporary variables */
+ out1 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+ out2 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+ out3 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+ out4 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+ /* Store the results of 4 inputs in the destination buffer in single cycle by packing */
+ *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4);
+
+ /* Decrement the blockSize loop counter */
+ blkCnt--;
+ }
+
+ /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+ ** No loop unrolling is used. */
+ blkCnt = blockSize % 0x4U;
+
+#else
+
+ /* Run the below code for Cortex-M0 */
+
+ /* Initialize blkCnt with number of samples */
+ blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+ while (blkCnt > 0U)
+ {
+ /* C = A * B */
+ /* Multiply the inputs and store the result in the destination buffer */
+ *pDst++ = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+ /* Decrement the blockSize loop counter */
+ blkCnt--;
+ }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c new file mode 100644 index 0000000..cabd9b1 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c @@ -0,0 +1,297 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nntables.c
+ * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @brief tables for various activation functions
+ *
+ * This file include the declaration of common tables.
+ * Most of them are used for activation functions
+ *
+ * Assumption:
+ * Unified table: input is 3.x format, i.e, range of [-8, 8)
+ * sigmoid(8) = 0.9996646498695336
+ * tanh(8) = 0.9999997749296758
+ * The accuracy here should be good enough
+ *
+ * 2-stage HL table:
+ *
+ * The entire input range is divided into two parts:
+ *
+ * Low range table: 0x000x xxxx or 0x111x xxxx
+ * table entry will be the binary number excluding the first
+ * two digits, i.e., 0x0x xxxx or 0x1x xxxx
+ *
+ *
+ *
+ * High range table 0x0010 0000 -- 0x0111 1111
+ * 0x1000 0000 -- 0x1101 1111
+ *
+ * For positive numbers, table entry will be
+ * 0x0010 0000 -- 0x0111 1111 minus 0x0010 0000
+ * i.e., 0x0000 0000 - 0x0101 11111
+ *
+ * same thing for the negative numbers, table entry will be
+ * 0x1000 0000 -- 0x1101 1111 minux 0x0010 0000
+ * i.e., 0x0110 0000 - 0x1011 1111
+ */
+
+const q7_t sigmoidTable_q7[256] = {
+ 0x40, 0x42, 0x44, 0x46, 0x48, 0x4a, 0x4c, 0x4e,
+ 0x50, 0x52, 0x53, 0x55, 0x57, 0x59, 0x5a, 0x5c,
+ 0x5e, 0x5f, 0x61, 0x62, 0x63, 0x65, 0x66, 0x67,
+ 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f, 0x70,
+ 0x71, 0x72, 0x72, 0x73, 0x74, 0x74, 0x75, 0x76,
+ 0x76, 0x77, 0x77, 0x78, 0x78, 0x79, 0x79, 0x7a,
+ 0x7a, 0x7a, 0x7b, 0x7b, 0x7b, 0x7c, 0x7c, 0x7c,
+ 0x7c, 0x7c, 0x7d, 0x7d, 0x7d, 0x7d, 0x7d, 0x7e,
+ 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+ 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+ 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+ 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x04,
+ 0x04, 0x04, 0x04, 0x04, 0x05, 0x05, 0x05, 0x06,
+ 0x06, 0x06, 0x07, 0x07, 0x08, 0x08, 0x09, 0x09,
+ 0x0a, 0x0a, 0x0b, 0x0c, 0x0c, 0x0d, 0x0e, 0x0e,
+ 0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16,
+ 0x17, 0x19, 0x1a, 0x1b, 0x1d, 0x1e, 0x1f, 0x21,
+ 0x22, 0x24, 0x26, 0x27, 0x29, 0x2b, 0x2d, 0x2e,
+ 0x30, 0x32, 0x34, 0x36, 0x38, 0x3a, 0x3c, 0x3e,
+};
+
+const q15_t sigmoidTable_q15[256] = {
+ 0x4000, 0x4200, 0x43ff, 0x45fc, 0x47f5, 0x49eb, 0x4bdc, 0x4dc8,
+ 0x4fad, 0x518a, 0x5360, 0x552c, 0x56ef, 0x58a8, 0x5a57, 0x5bfb,
+ 0x5d93, 0x5f20, 0x60a1, 0x6216, 0x637f, 0x64db, 0x662b, 0x676f,
+ 0x68a6, 0x69d2, 0x6af1, 0x6c05, 0x6d0d, 0x6e09, 0x6efb, 0x6fe2,
+ 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+ 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+ 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+ 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+ 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+ 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+ 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+ 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+ 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+ 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+ 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+ 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+ 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+ 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+ 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+ 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+ 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+ 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+ 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+ 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+ 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+ 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+ 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+ 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+ 0x0f42, 0x101e, 0x1105, 0x11f7, 0x12f3, 0x13fb, 0x150f, 0x162e,
+ 0x175a, 0x1891, 0x19d5, 0x1b25, 0x1c81, 0x1dea, 0x1f5f, 0x20e0,
+ 0x226d, 0x2405, 0x25a9, 0x2758, 0x2911, 0x2ad4, 0x2ca0, 0x2e76,
+ 0x3053, 0x3238, 0x3424, 0x3615, 0x380b, 0x3a04, 0x3c01, 0x3e00,
+};
+
+const q15_t sigmoidLTable_q15[128] = {
+ 0x4000, 0x4100, 0x4200, 0x42ff, 0x43ff, 0x44fd, 0x45fc, 0x46f9,
+ 0x47f5, 0x48f1, 0x49eb, 0x4ae5, 0x4bdc, 0x4cd3, 0x4dc8, 0x4ebb,
+ 0x4fad, 0x509c, 0x518a, 0x5276, 0x5360, 0x5447, 0x552c, 0x560f,
+ 0x56ef, 0x57cd, 0x58a8, 0x5981, 0x5a57, 0x5b2a, 0x5bfb, 0x5cc9,
+ 0x5d93, 0x5e5b, 0x5f20, 0x5fe2, 0x60a1, 0x615d, 0x6216, 0x62cc,
+ 0x637f, 0x642e, 0x64db, 0x6584, 0x662b, 0x66ce, 0x676f, 0x680c,
+ 0x68a6, 0x693d, 0x69d2, 0x6a63, 0x6af1, 0x6b7c, 0x6c05, 0x6c8a,
+ 0x6d0d, 0x6d8d, 0x6e09, 0x6e84, 0x6efb, 0x6f70, 0x6fe2, 0x7051,
+ 0x0f42, 0x0faf, 0x101e, 0x1090, 0x1105, 0x117c, 0x11f7, 0x1273,
+ 0x12f3, 0x1376, 0x13fb, 0x1484, 0x150f, 0x159d, 0x162e, 0x16c3,
+ 0x175a, 0x17f4, 0x1891, 0x1932, 0x19d5, 0x1a7c, 0x1b25, 0x1bd2,
+ 0x1c81, 0x1d34, 0x1dea, 0x1ea3, 0x1f5f, 0x201e, 0x20e0, 0x21a5,
+ 0x226d, 0x2337, 0x2405, 0x24d6, 0x25a9, 0x267f, 0x2758, 0x2833,
+ 0x2911, 0x29f1, 0x2ad4, 0x2bb9, 0x2ca0, 0x2d8a, 0x2e76, 0x2f64,
+ 0x3053, 0x3145, 0x3238, 0x332d, 0x3424, 0x351b, 0x3615, 0x370f,
+ 0x380b, 0x3907, 0x3a04, 0x3b03, 0x3c01, 0x3d01, 0x3e00, 0x3f00,
+};
+
+const q15_t sigmoidHTable_q15[192] = {
+ 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+ 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+ 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+ 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+ 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+ 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+ 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+ 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+ 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+ 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+ 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+ 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+ 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+ 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+ 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+ 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+ 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+ 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+ 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+ 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+ 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+ 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+ 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+ 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+};
+
+const q7_t tanhTable_q7[256] = {
+ 0x00, 0x08, 0x10, 0x18, 0x1f, 0x27, 0x2e, 0x35,
+ 0x3b, 0x41, 0x47, 0x4c, 0x51, 0x56, 0x5a, 0x5e,
+ 0x61, 0x65, 0x68, 0x6a, 0x6d, 0x6f, 0x71, 0x72,
+ 0x74, 0x75, 0x76, 0x78, 0x78, 0x79, 0x7a, 0x7b,
+ 0x7b, 0x7c, 0x7c, 0x7d, 0x7d, 0x7e, 0x7e, 0x7e,
+ 0x7e, 0x7e, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x81,
+ 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x82,
+ 0x82, 0x82, 0x82, 0x82, 0x83, 0x83, 0x84, 0x84,
+ 0x85, 0x85, 0x86, 0x87, 0x88, 0x88, 0x8a, 0x8b,
+ 0x8c, 0x8e, 0x8f, 0x91, 0x93, 0x96, 0x98, 0x9b,
+ 0x9f, 0xa2, 0xa6, 0xaa, 0xaf, 0xb4, 0xb9, 0xbf,
+ 0xc5, 0xcb, 0xd2, 0xd9, 0xe1, 0xe8, 0xf0, 0xf8,
+};
+
+const q15_t tanhTable_q15[256] = {
+ 0x0000, 0x07fd, 0x0feb, 0x17b9, 0x1f59, 0x26bf, 0x2ddf, 0x34ae,
+ 0x3b27, 0x4142, 0x46fd, 0x4c56, 0x514d, 0x55e2, 0x5a1a, 0x5df6,
+ 0x617c, 0x64b0, 0x6797, 0x6a37, 0x6c95, 0x6eb5, 0x709e, 0x7254,
+ 0x73dc, 0x753a, 0x7672, 0x7788, 0x787f, 0x795b, 0x7a1e, 0x7acb,
+ 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+ 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+ 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+ 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+ 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+ 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+ 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+ 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+ 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+ 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+ 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+ 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+ 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+ 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+ 0x849b, 0x8535, 0x85e2, 0x86a5, 0x8781, 0x8878, 0x898e, 0x8ac6,
+ 0x8c24, 0x8dac, 0x8f62, 0x914b, 0x936b, 0x95c9, 0x9869, 0x9b50,
+ 0x9e84, 0xa20a, 0xa5e6, 0xaa1e, 0xaeb3, 0xb3aa, 0xb903, 0xbebe,
+ 0xc4d9, 0xcb52, 0xd221, 0xd941, 0xe0a7, 0xe847, 0xf015, 0xf803,
+};
+
+const q15_t tanhLTable_q15[128] = {
+ 0x0000, 0x0400, 0x07fd, 0x0bf7, 0x0feb, 0x13d7, 0x17b9, 0x1b90,
+ 0x1f59, 0x2314, 0x26bf, 0x2a58, 0x2ddf, 0x3151, 0x34ae, 0x37f6,
+ 0x3b27, 0x3e40, 0x4142, 0x442c, 0x46fd, 0x49b6, 0x4c56, 0x4edd,
+ 0x514d, 0x53a3, 0x55e2, 0x580a, 0x5a1a, 0x5c13, 0x5df6, 0x5fc4,
+ 0x617c, 0x6320, 0x64b0, 0x662d, 0x6797, 0x68f0, 0x6a37, 0x6b6e,
+ 0x6c95, 0x6dac, 0x6eb5, 0x6fb0, 0x709e, 0x717f, 0x7254, 0x731e,
+ 0x73dc, 0x7490, 0x753a, 0x75da, 0x7672, 0x7701, 0x7788, 0x7807,
+ 0x787f, 0x78f0, 0x795b, 0x79bf, 0x7a1e, 0x7a77, 0x7acb, 0x7b1b,
+ 0x849b, 0x84e5, 0x8535, 0x8589, 0x85e2, 0x8641, 0x86a5, 0x8710,
+ 0x8781, 0x87f9, 0x8878, 0x88ff, 0x898e, 0x8a26, 0x8ac6, 0x8b70,
+ 0x8c24, 0x8ce2, 0x8dac, 0x8e81, 0x8f62, 0x9050, 0x914b, 0x9254,
+ 0x936b, 0x9492, 0x95c9, 0x9710, 0x9869, 0x99d3, 0x9b50, 0x9ce0,
+ 0x9e84, 0xa03c, 0xa20a, 0xa3ed, 0xa5e6, 0xa7f6, 0xaa1e, 0xac5d,
+ 0xaeb3, 0xb123, 0xb3aa, 0xb64a, 0xb903, 0xbbd4, 0xbebe, 0xc1c0,
+ 0xc4d9, 0xc80a, 0xcb52, 0xceaf, 0xd221, 0xd5a8, 0xd941, 0xdcec,
+ 0xe0a7, 0xe470, 0xe847, 0xec29, 0xf015, 0xf409, 0xf803, 0xfc00,
+};
+
+const q15_t tanhHTable_q15[192] = {
+ 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+ 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+ 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+ 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+ 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+ 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+ 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+ 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+ 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+ 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+ 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+ 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+ 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+ 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+ 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+};
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c new file mode 100644 index 0000000..e043b38 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c @@ -0,0 +1,134 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_q7_to_q15_no_shift.c
+ * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup nndata_convert
+ * @{
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ * \par Description:
+ *
+ * The equation used for the conversion process is:
+ *
+ * <pre>
+ * pDst[n] = (q15_t) pSrc[n]; 0 <= n < blockSize.
+ * </pre>
+ *
+ */
+
+void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+ const q7_t *pIn = pSrc; /* Src pointer */
+ uint32_t blkCnt; /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+ q31_t in;
+ q31_t in1, in2;
+ q31_t out1, out2;
+
+ /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+ /*loop Unrolling */
+ blkCnt = blockSize >> 2u;
+
+ /* First part of the processing with loop unrolling. Compute 4 outputs at a time.
+ ** a second loop below computes the remaining 1 to 3 samples. */
+ while (blkCnt > 0u)
+ {
+ /* C = (q15_t) A << 8 */
+ /* convert from q7 to q15 and then store the results in the destination buffer */
+ in = *__SIMD32(pIn)++;
+
+ /* rotatate in by 8 and extend two q7_t values to q15_t values */
+ in1 = __SXTB16(__ROR(in, 8));
+
+ /* extend remainig two q7_t values to q15_t values */
+ in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ out2 = __PKHTB(in1, in2, 16);
+ out1 = __PKHBT(in2, in1, 16);
+
+#else
+
+ out1 = __PKHTB(in1, in2, 16);
+ out2 = __PKHBT(in2, in1, 16);
+
+#endif
+
+ *__SIMD32(pDst)++ = out1;
+ *__SIMD32(pDst)++ = out2;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+ /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+ ** No loop unrolling is used. */
+ blkCnt = blockSize % 0x4u;
+
+#else
+
+ /* Run the below code for Cortex-M0 */
+
+ /* Loop over blockSize number of values */
+ blkCnt = blockSize;
+
+#endif /* #ifndef ARM_MATH_CM0_FAMILY */
+
+ while (blkCnt > 0u)
+ {
+ /* C = (q15_t) A << 8 */
+ /* convert from q7 to q15 and then store the results in the destination buffer */
+ *pDst++ = (q15_t) * pIn++;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+}
+
+/**
+ * @} end of nndata_convert group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c new file mode 100644 index 0000000..52f5f8e --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c @@ -0,0 +1,145 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_q7_to_q15_reordered_no_shift.c
+ * Description: Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup nndata_convert
+ * @{
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ * @details
+ *
+ * This function does the q7 to q15 expansion with re-ordering
+ *
+ * <pre>
+ * | A1 | A2 | A3 | A4 |
+ *
+ * 0 7 8 15 16 23 24 31
+ * </pre>
+ *
+ * is converted into:
+ *
+ * <pre>
+ * | A1 | A3 | and | A2 | A4 |
+ *
+ * 0 15 16 31 0 15 16 31
+ * </pre>
+ *
+ *
+ * This looks strange but is natural considering how sign-extension is done at
+ * assembly level.
+ *
+ * The expansion of other other oprand will follow the same rule so that the end
+ * results are the same.
+ *
+ * The tail (i.e., last (N % 4) elements) will still be in original order.
+ *
+ */
+
+void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+ const q7_t *pIn = pSrc; /* Src pointer */
+ uint32_t blkCnt; /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+ q31_t in;
+ q31_t in1, in2;
+
+ /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+ /*loop Unrolling */
+ blkCnt = blockSize >> 2u;
+
+ /* First part of the processing with loop unrolling. Compute 4 outputs at a time.
+ ** a second loop below computes the remaining 1 to 3 samples. */
+ while (blkCnt > 0u)
+ {
+ /* C = (q15_t) A << 8 */
+ /* convert from q7 to q15 and then store the results in the destination buffer */
+ in = *__SIMD32(pIn)++;
+
+ /* rotatate in by 8 and extend two q7_t values to q15_t values */
+ in1 = __SXTB16(__ROR(in, 8));
+
+ /* extend remainig two q7_t values to q15_t values */
+ in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ *__SIMD32(pDst)++ = in2;
+ *__SIMD32(pDst)++ = in1;
+#else
+ *__SIMD32(pDst)++ = in1;
+ *__SIMD32(pDst)++ = in2;
+#endif
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+ /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+ ** No loop unrolling is used. */
+ blkCnt = blockSize % 0x4u;
+
+#else
+
+ /* Run the below code for Cortex-M0 */
+
+ /* Loop over blockSize number of values */
+ blkCnt = blockSize;
+
+#endif /* #ifndef ARM_MATH_CM0_FAMILY */
+
+ while (blkCnt > 0u)
+ {
+ /* C = (q15_t) A << 8 */
+ /* convert from q7 to q15 and then store the results in the destination buffer */
+ *pDst++ = (q15_t) * pIn++;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+}
+
+/**
+ * @} end of q7_to_x group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c new file mode 100644 index 0000000..2759731 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c @@ -0,0 +1,448 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_pool_q7_HWC.c
+ * Description: Pooling function implementations
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief A few utility functions used by pooling functions
+ *
+ *
+ */
+
+static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale)
+{
+ int i;
+
+ for (i = 0; i < length; i++)
+ {
+ target[i] = (q7_t) (buffer[i] / scale);
+ }
+}
+
+static void compare_and_replace_if_larger_q7(q7_t * base, // base data
+ q7_t * target, // compare target
+ const uint16_t length // data length
+ )
+{
+ q7_t *pIn = base;
+ q7_t *pCom = target;
+ union arm_nnword in;
+ union arm_nnword com;
+ uint16_t cnt = length >> 2;
+
+ while (cnt > 0u)
+ {
+ in.word = *__SIMD32(pIn);
+ com.word = *__SIMD32(pCom)++;
+
+ // if version
+ if (com.bytes[0] > in.bytes[0])
+ in.bytes[0] = com.bytes[0];
+ if (com.bytes[1] > in.bytes[1])
+ in.bytes[1] = com.bytes[1];
+ if (com.bytes[2] > in.bytes[2])
+ in.bytes[2] = com.bytes[2];
+ if (com.bytes[3] > in.bytes[3])
+ in.bytes[3] = com.bytes[3];
+
+ *__SIMD32(pIn)++ = in.word;
+
+ cnt--;
+ }
+}
+
+static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length)
+{
+ q15_t *pCnt = base;
+ q7_t *pV = target;
+ q31_t v1, v2, vo1, vo2;
+ uint16_t cnt = length >> 2;
+ q31_t in;
+
+ while (cnt > 0u)
+ {
+ q31_t value = *__SIMD32(pV)++;
+ v1 = __SXTB16(__ROR(value, 8));
+ v2 = __SXTB16(value);
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ vo2 = __PKHTB(v1, v2, 16);
+ vo1 = __PKHBT(v2, v1, 16);
+
+#else
+
+ vo1 = __PKHTB(v1, v2, 16);
+ vo2 = __PKHBT(v2, v1, 16);
+
+#endif
+
+ in = *__SIMD32(pCnt);
+ *__SIMD32(pCnt)++ = __QADD16(vo1, in);
+
+ in = *__SIMD32(pCnt);
+ *__SIMD32(pCnt)++ = __QADD16(vo2, in);
+
+ cnt--;
+ }
+ cnt = length & 0x3;
+ while (cnt > 0u)
+ {
+ *pCnt++ += *pV++;
+ cnt--;
+ }
+}
+
+#endif // ARM_MATH_DSP
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Pooling
+ * @{
+ */
+
+ /**
+ * @brief Q7 max pooling function
+ * @param[in, out] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 0
+ *
+ * The pooling function is implemented as split x-pooling then
+ * y-pooling.
+ *
+ * This pooling function is input-destructive. Input data is undefined
+ * after calling this function.
+ *
+ */
+
+void
+arm_maxpool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_x, i_y;
+
+ /* first does the pooling along x axis */
+ for (i_y = 0; i_y < dim_im_in; i_y++)
+ {
+
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ /* for each output pixel */
+ q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+ q7_t *win_start;
+ q7_t *win_stop;
+ if (i_x * stride - padding < 0)
+ {
+ win_start = target;
+ } else
+ {
+ win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+ }
+
+ if (i_x * stride - padding + dim_kernel >= dim_im_in)
+ {
+ win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+ } else
+ {
+ win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+ }
+
+ /* first step is to copy over initial data */
+ /* arm_copy_q7(win_start, target, ch_im_in); */
+ memmove(target, win_start, ch_im_in);
+
+ /* start the max operation from the second part */
+ win_start += ch_im_in;
+ for (; win_start < win_stop; win_start += ch_im_in)
+ {
+ compare_and_replace_if_larger_q7(target, win_start, ch_im_in);
+ }
+ }
+ }
+
+ /* then does the pooling along y axis */
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+
+ /* for each output row */
+ q7_t *target = Im_out + i_y * dim_im_out * ch_im_in;
+ q7_t *row_start;
+ q7_t *row_end;
+ /* setting the starting row */
+ if (i_y * stride - padding < 0)
+ {
+ row_start = Im_in;
+ } else
+ {
+ row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+ }
+ /* setting the stopping row */
+ if (i_y * stride - padding + dim_kernel >= dim_im_in)
+ {
+ row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+ } else
+ {
+ row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+ }
+
+ /* copy over the first row */
+ /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */
+ memmove(target, row_start, dim_im_out * ch_im_in);
+
+ /* move over to next row */
+ row_start += ch_im_in * dim_im_in;
+
+ for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+ {
+ compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in);
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int max = -129;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+ {
+ max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ }
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+ /**
+ * @brief Q7 average pooling function
+ * @param[in,out] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*dim_im_out*ch_im_in
+ *
+ * The pooling function is implemented as split x-pooling then
+ * y-pooling.
+ *
+ * This pooling function is input-destructive. Input data is undefined
+ * after calling this function.
+ *
+ */
+
+void
+arm_avepool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ q15_t *buffer = (q15_t *) bufferA;
+ int16_t i_x, i_y;
+ int16_t count = 0;
+
+ /* first does the pooling along x axis */
+ for (i_y = 0; i_y < dim_im_in; i_y++)
+ {
+
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ /* for each output pixel */
+ q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+ q7_t *win_start;
+ q7_t *win_stop;
+ if (i_x * stride - padding < 0)
+ {
+ win_start = target;
+ } else
+ {
+ win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+ }
+
+ if (i_x * stride - padding + dim_kernel >= dim_im_in)
+ {
+ win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+ } else
+ {
+ win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+ }
+
+ /* first step is to copy over initial data */
+ arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in);
+ count = 1;
+
+ /* start the max operation from the second part */
+ win_start += ch_im_in;
+ for (; win_start < win_stop; win_start += ch_im_in)
+ {
+ accumulate_q7_to_q15(buffer, win_start, ch_im_in);
+ count++;
+ }
+ buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count);
+ }
+ }
+
+ /* then does the pooling along y axis */
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ /* for each output row */
+ q7_t *target = Im_out + i_y * dim_im_out * ch_im_in;
+ q7_t *row_start;
+ q7_t *row_end;
+ /* setting the starting row */
+ if (i_y * stride - padding < 0)
+ {
+ row_start = Im_in;
+ } else
+ {
+ row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+ }
+ /* setting the stopping row */
+ if (i_y * stride - padding + dim_kernel >= dim_im_in)
+ {
+ row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+ } else
+ {
+ row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+ }
+
+ /* copy over the first row */
+ arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in);
+ count = 1;
+
+ /* move over to next row */
+ row_start += ch_im_in * dim_im_in;
+
+ for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+ {
+ accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in);
+ count++;
+ }
+ buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count);
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int sum = 0;
+ int count = 0;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ count++;
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Pooling group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c new file mode 100644 index 0000000..22fa62b --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c @@ -0,0 +1,120 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_softmax_q15.c
+ * Description: Q15 softmax function
+ *
+ * $Date: 20. February 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+ /**
+ * @brief Q15 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ * @details
+ *
+ * Here, instead of typical e based softmax, we use
+ * 2-based softmax, i.e.,:
+ *
+ * y_i = 2^(x_i) / sum(2^x_j)
+ *
+ * The relative output will be different here.
+ * But mathematically, the gradient will be the same
+ * with a log(2) scaling factor.
+ *
+ */
+
+void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
+{
+ q31_t sum;
+ int16_t i;
+ uint8_t shift;
+ q31_t base;
+ base = -1 * 0x100000;
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ base = vec_in[i];
+ }
+ }
+
+ /* we ignore really small values
+ * anyway, they will be 0 after shrinking
+ * to q15_t
+ */
+ base = base - 16;
+
+ sum = 0;
+
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+ sum += 0x1 << shift;
+ }
+ }
+
+ /* This is effectively (0x1 << 32) / sum */
+ int64_t div_base = 0x100000000LL;
+ int output_base = (int32_t)(div_base / sum);
+
+ /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
+ * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
+ * and vec_in[i]-base = 16
+ */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ /* Here minimum value of 17+base-vec[i] will be 1 */
+ shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
+ p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
+ } else
+ {
+ p_out[i] = 0;
+ }
+ }
+
+}
+
+/**
+ * @} end of Softmax group
+ */
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c new file mode 100644 index 0000000..06a69e1 --- /dev/null +++ b/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c @@ -0,0 +1,121 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_softmax_q7.c
+ * Description: Q7 softmax function
+ *
+ * $Date: 20. February 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+ /**
+ * @brief Q7 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ * @details
+ *
+ * Here, instead of typical natural logarithm e based softmax, we use
+ * 2-based softmax here, i.e.,:
+ *
+ * y_i = 2^(x_i) / sum(2^x_j)
+ *
+ * The relative output will be different here.
+ * But mathematically, the gradient will be the same
+ * with a log(2) scaling factor.
+ *
+ */
+
+void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
+{
+ q31_t sum;
+ int16_t i;
+ uint8_t shift;
+ q15_t base;
+ base = -257;
+
+ /* We first search for the maximum */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ base = vec_in[i];
+ }
+ }
+
+ /*
+ * So the base is set to max-8, meaning
+ * that we ignore really small values.
+ * anyway, they will be 0 after shrinking to q7_t.
+ */
+ base = base - 8;
+
+ sum = 0;
+
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+ sum += 0x1 << shift;
+ }
+ }
+
+ /* This is effectively (0x1 << 20) / sum */
+ int output_base = 0x100000 / sum;
+
+ /*
+ * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
+ * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12
+ * and vec_in[i]-base = 8
+ */
+ for (i = 0; i < dim_vec; i++)
+ {
+ if (vec_in[i] > base)
+ {
+ /* Here minimum value of 13+base-vec_in[i] will be 5 */
+ shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
+ p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
+ } else {
+ p_out[i] = 0;
+ }
+ }
+}
+
+/**
+ * @} end of Softmax group
+ */
|