diff options
Diffstat (limited to 'fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions')
13 files changed, 0 insertions, 3640 deletions
diff --git a/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 4c69e7c..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c deleted file mode 100644 index ee08d74..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c deleted file mode 100644 index a02aaa0..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c deleted file mode 100644 index 14d9130..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c deleted file mode 100644 index e53c6f9..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c deleted file mode 100644 index 7c9ec65..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c deleted file mode 100644 index 24356d9..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c deleted file mode 100644 index e2d469f..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 6dc6f0b..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c deleted file mode 100644 index 705fa6a..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c deleted file mode 100644 index 5989304..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c deleted file mode 100644 index 24ab412..0000000 --- a/fw/midi-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/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c deleted file mode 100644 index 36af21a..0000000 --- a/fw/midi-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 */
-}
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