diff options
Diffstat (limited to 'fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c')
| -rw-r--r-- | fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c | 408 |
1 files changed, 0 insertions, 408 deletions
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c deleted file mode 100644 index e2d469f..0000000 --- a/fw/hid-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c +++ /dev/null @@ -1,408 +0,0 @@ -/*
- * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the License); you may
- * not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* ----------------------------------------------------------------------
- * Project: CMSIS NN Library
- * Title: arm_convolve_HWC_q7_fast.c
- * Description: Fast Q7 version of convolution
- *
- * $Date: 17. January 2018
- * $Revision: V.1.0.0
- *
- * Target Processor: Cortex-M cores
- *
- * -------------------------------------------------------------------- */
-
-#include "arm_math.h"
-#include "arm_nnfunctions.h"
-
-/**
- * @ingroup groupNN
- */
-
-/**
- * @addtogroup NNConv
- * @{
- */
-
- /**
- * @brief Fast Q7 convolution function
- * @param[in] Im_in pointer to input tensor
- * @param[in] dim_im_in input tensor dimention
- * @param[in] ch_im_in number of input tensor channels
- * @param[in] wt pointer to kernel weights
- * @param[in] ch_im_out number of filters, i.e., output tensor channels
- * @param[in] dim_kernel filter kernel size
- * @param[in] padding padding sizes
- * @param[in] stride convolution stride
- * @param[in] bias pointer to bias
- * @param[in] bias_shift amount of left-shift for bias
- * @param[in] out_shift amount of right-shift for output
- * @param[in,out] Im_out pointer to output tensor
- * @param[in] dim_im_out output tensor dimension
- * @param[in,out] bufferA pointer to buffer space for input
- * @param[in,out] bufferB pointer to buffer space for output
- * @return The function returns either
- * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
- *
- * @details
- *
- * <b>Buffer size:</b>
- *
- * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
- *
- * bufferB size: 0
- *
- * <b>Input dimension constraints:</b>
- *
- * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap )
- *
- * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel )
- *
- * The im2col converts the Q7 tensor input into Q15 column, which is stored in
- * bufferA. There is reordering happenning during this im2col process with
- * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and
- * third elements are swapped.
- *
- * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the
- * GEMM computation with the reordered columns.
- *
- * To speed-up the determination of the padding condition, we split the
- * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}.
- * This reduces the total number of boundary condition checks and improves
- * the data copying performance.
- */
-
-arm_status
-arm_convolve_HWC_q7_fast(const q7_t * Im_in,
- const uint16_t dim_im_in,
- const uint16_t ch_im_in,
- const q7_t * wt,
- const uint16_t ch_im_out,
- const uint16_t dim_kernel,
- const uint16_t padding,
- const uint16_t stride,
- const q7_t * bias,
- const uint16_t bias_shift,
- const uint16_t out_shift,
- q7_t * Im_out,
- const uint16_t dim_im_out,
- q15_t * bufferA,
- q7_t * bufferB)
-{
-
-#if defined (ARM_MATH_DSP)
- /* Run the following code for Cortex-M4 and Cortex-M7 */
-
- int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
-
- /*
- * Here we use bufferA as q15_t internally as computation are done with q15_t level
- * im2col are done to output in q15_t format from q7_t input
- */
-
- q15_t *pBuffer = bufferA;
- q7_t *pOut = Im_out;
-
- if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
- {
- /* check if the input dimension meets the constraints */
- return ARM_MATH_SIZE_MISMATCH;
- }
-
- /*
- * Here we split the entire matrix into three regions depending on the padding situation
- * Top: i_out_y from 0 to padding - 1
- * Middle: i_out_y from padding to dim_im_out-padding-1
- * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
- */
-
- /* top part */
- for (i_out_y = 0; i_out_y < padding; i_out_y++)
- {
- for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
- {
- /* This part implements the im2col function */
- for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
- {
- for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
- {
- if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
- {
- /* arm_fill_q15(0, pBuffer, ch_im_in); */
- memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
- } else
- {
- arm_q7_to_q15_reordered_no_shift
- ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
- }
- pBuffer += ch_im_in;
- }
- }
-
- if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
- {
- pOut =
- arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
- bufferA,
- ch_im_out,
- ch_im_in
- *
- dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
- /* counter reset */
- pBuffer = bufferA;
- }
- }
- }
-
- /* middle part, here we also divide the x into left, mid and right */
- for (; i_out_y < dim_im_out - padding; i_out_y++)
- {
-
- /* left part */
- for (i_out_x = 0; i_out_x < padding; i_out_x++)
- {
- /* This part implements the im2col function */
- for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
- {
- for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
- {
- if (i_ker_x < 0 || i_ker_x >= dim_im_in)
- {
- /* arm_fill_q15(0, pBuffer, ch_im_in); */
- memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
- } else
- {
- arm_q7_to_q15_reordered_no_shift
- ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
- }
- pBuffer += ch_im_in;
- }
- }
-
- if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
- {
- pOut =
- arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
- bufferA,
- ch_im_out,
- ch_im_in
- *
- dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
- /* counter reset */
- pBuffer = bufferA;
- }
- }
-
- /* mid part */
- for (; i_out_x < dim_im_out - padding; i_out_x++)
- {
- /* This part implements the im2col function */
- for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
- {
- arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in
- +
- (i_ker_y *
- dim_im_in +
- i_out_x *
- stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel);
- pBuffer += ch_im_in * dim_kernel;
- }
-
- if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
- {
- pOut =
- arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
- bufferA,
- ch_im_out,
- ch_im_in
- *
- dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
- /* counter reset */
- pBuffer = bufferA;
- }
- }
-
- /* right part */
- for (; i_out_x < dim_im_out; i_out_x++)
- {
- /* This part implements the im2col function */
- for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
- {
- for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
- {
- if (i_ker_x < 0 || i_ker_x >= dim_im_in)
- {
- /* arm_fill_q15(0, pBuffer, ch_im_in); */
- memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
- } else
- {
- arm_q7_to_q15_reordered_no_shift
- ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
- }
- pBuffer += ch_im_in;
- }
- }
-
- if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
- {
- pOut =
- arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
- bufferA,
- ch_im_out,
- ch_im_in
- *
- dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
- /* counter reset */
- pBuffer = bufferA;
- }
- }
- }
-
- for (; i_out_y < dim_im_out; i_out_y++)
- {
- for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
- {
- /* This part implements the im2col function */
- for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
- {
- for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
- {
- if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
- {
- /* arm_fill_q15(0, pBuffer, ch_im_in); */
- memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
- } else
- {
- arm_q7_to_q15_reordered_no_shift
- ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
- }
- pBuffer += ch_im_in;
- }
- }
-
- if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
- {
- pOut =
- arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
- bufferA,
- ch_im_out,
- ch_im_in
- *
- dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
- /* counter reset */
- pBuffer = bufferA;
- }
- }
- }
-
- /* check if there is left-over for compute */
- if (pBuffer != bufferA)
- {
- const q7_t *pA = wt;
- int i;
-
- for (i = 0; i < ch_im_out; i++)
- {
- q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
- q15_t *pB = bufferA;
- /* each time it process 4 entries */
- uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
-
- while (colCnt)
- {
-
- q31_t inA1, inA2;
- q31_t inB1, inB2;
-
- pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2);
-
- inB1 = *__SIMD32(pB)++;
- sum = __SMLAD(inA1, inB1, sum);
- inB2 = *__SIMD32(pB)++;
- sum = __SMLAD(inA2, inB2, sum);
-
- colCnt--;
- }
- colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
- while (colCnt)
- {
- q7_t inA1 = *pA++;
- q15_t inB1 = *pB++;
- sum += inA1 * inB1;
- colCnt--;
- }
- *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
- pOut++;
-
- }
-
- }
-#else
- /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
-
- uint16_t i, j, k, l, m, n;
- int conv_out;
- signed char in_row, in_col;
-
- if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
- {
- /* check if the input dimension meets the constraints */
- return ARM_MATH_SIZE_MISMATCH;
- }
-
- for (i = 0; i < ch_im_out; i++)
- {
- for (j = 0; j < dim_im_out; j++)
- {
- for (k = 0; k < dim_im_out; k++)
- {
- conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
- for (m = 0; m < dim_kernel; m++)
- {
- for (n = 0; n < dim_kernel; n++)
- {
- // if-for implementation
- in_row = stride * j + m - padding;
- in_col = stride * k + n - padding;
- if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
- {
- for (l = 0; l < ch_im_in; l++)
- {
- conv_out +=
- Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
- l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
- n) * ch_im_in + l];
- }
- }
- }
- }
- Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
- }
- }
- }
-
-#endif /* ARM_MATH_DSP */
-
- /* Return to application */
- return ARM_MATH_SUCCESS;
-}
-
-/**
- * @} end of NNConv group
- */
|