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| author | jaseg <git@jaseg.de> | 2020-12-21 16:22:11 +0100 |
|---|---|---|
| committer | jaseg <git@jaseg.de> | 2020-12-21 16:22:11 +0100 |
| commit | d8b6d18d2fefee381eb6a26334a13e94126b6d8e (patch) | |
| tree | ab3a005e80c7b3d5b5d727ec04a2cbeefc3d51e6 /fw/hid-dials/Drivers/CMSIS/NN/Source | |
| parent | 26f5fefac6f15db1c8c81fb78a919d35211695c4 (diff) | |
| download | minikbd-d8b6d18d2fefee381eb6a26334a13e94126b6d8e.tar.gz minikbd-d8b6d18d2fefee381eb6a26334a13e94126b6d8e.tar.bz2 minikbd-d8b6d18d2fefee381eb6a26334a13e94126b6d8e.zip | |
Remove obsolete template code from fw
Diffstat (limited to 'fw/hid-dials/Drivers/CMSIS/NN/Source')
31 files changed, 0 insertions, 7388 deletions
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 deleted file mode 100644 index fd447e5..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c +++ /dev/null @@ -1,101 +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_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 deleted file mode 100644 index 2953bd5..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c +++ /dev/null @@ -1,91 +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_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 deleted file mode 100644 index 6a1b907..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c +++ /dev/null @@ -1,106 +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_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 deleted file mode 100644 index caa027b..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c +++ /dev/null @@ -1,110 +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_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 deleted file mode 100644 index 4c69e7c..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,235 +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_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 deleted file mode 100644 index ee08d74..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c +++ /dev/null @@ -1,207 +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_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 deleted file mode 100644 index a02aaa0..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c +++ /dev/null @@ -1,255 +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_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 deleted file mode 100644 index 14d9130..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c +++ /dev/null @@ -1,265 +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_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 deleted file mode 100644 index e53c6f9..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c +++ /dev/null @@ -1,279 +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_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 deleted file mode 100644 index 7c9ec65..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c +++ /dev/null @@ -1,230 +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_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 deleted file mode 100644 index 24356d9..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c +++ /dev/null @@ -1,228 +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_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 deleted file mode 100644 index e2d469f..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c +++ /dev/null @@ -1,408 +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_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 deleted file mode 100644 index 6dc6f0b..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,379 +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_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 deleted file mode 100644 index 705fa6a..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c +++ /dev/null @@ -1,418 +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_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 deleted file mode 100644 index 5989304..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c +++ /dev/null @@ -1,411 +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_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 deleted file mode 100644 index 24ab412..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c +++ /dev/null @@ -1,187 +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_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 deleted file mode 100644 index 36af21a..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c +++ /dev/null @@ -1,138 +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_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 deleted file mode 100644 index bb9a091..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c +++ /dev/null @@ -1,199 +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_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 deleted file mode 100644 index b0c308b..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c +++ /dev/null @@ -1,403 +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_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 deleted file mode 100644 index a4c6bba..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c +++ /dev/null @@ -1,193 +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_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 deleted file mode 100644 index 8f3bbea..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c +++ /dev/null @@ -1,332 +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_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 deleted file mode 100644 index 75e924f..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c +++ /dev/null @@ -1,198 +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_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 deleted file mode 100644 index d197adc..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c +++ /dev/null @@ -1,484 +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_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 deleted file mode 100644 index de7668b..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c +++ /dev/null @@ -1,147 +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_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 deleted file mode 100644 index 1b4e02c..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c +++ /dev/null @@ -1,119 +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_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 deleted file mode 100644 index cabd9b1..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c +++ /dev/null @@ -1,297 +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_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 deleted file mode 100644 index e043b38..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c +++ /dev/null @@ -1,134 +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_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 deleted file mode 100644 index 52f5f8e..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c +++ /dev/null @@ -1,145 +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_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 deleted file mode 100644 index 2759731..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c +++ /dev/null @@ -1,448 +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_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 deleted file mode 100644 index 22fa62b..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c +++ /dev/null @@ -1,120 +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_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 deleted file mode 100644 index 06a69e1..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c +++ /dev/null @@ -1,121 +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_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
- */
|