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| author | jaseg <git@jaseg.net> | 2020-10-14 12:47:28 +0200 |
|---|---|---|
| committer | jaseg <git@jaseg.net> | 2020-10-14 12:47:28 +0200 |
| commit | 6ab94e0b318884bbcb95e2ea3835f951502e1d99 (patch) | |
| tree | 4cc5794b89f89c55ff8370ae252518ab96b5fed3 /hid-dials/Drivers/CMSIS/NN/Include | |
| parent | 1e6e8a2062923b434b2f4f5f2a9f8c0098135b01 (diff) | |
| download | minikbd-6ab94e0b318884bbcb95e2ea3835f951502e1d99.tar.gz minikbd-6ab94e0b318884bbcb95e2ea3835f951502e1d99.tar.bz2 minikbd-6ab94e0b318884bbcb95e2ea3835f951502e1d99.zip | |
Move firmware into subdirectory
Diffstat (limited to 'hid-dials/Drivers/CMSIS/NN/Include')
| -rw-r--r-- | hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h | 59 | ||||
| -rw-r--r-- | hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h | 1010 | ||||
| -rw-r--r-- | hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h | 202 |
3 files changed, 0 insertions, 1271 deletions
diff --git a/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h b/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h deleted file mode 100644 index 9357424..0000000 --- a/hid-dials/Drivers/CMSIS/NN/Include/arm_nn_tables.h +++ /dev/null @@ -1,59 +0,0 @@ -/* ----------------------------------------------------------------------
- * 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/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h b/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h deleted file mode 100644 index 96c59c2..0000000 --- a/hid-dials/Drivers/CMSIS/NN/Include/arm_nnfunctions.h +++ /dev/null @@ -1,1010 +0,0 @@ -/*
- * 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/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h b/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h deleted file mode 100644 index 05a239d..0000000 --- a/hid-dials/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h +++ /dev/null @@ -1,202 +0,0 @@ -/*
- * 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
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