From 6ab94e0b318884bbcb95e2ea3835f951502e1d99 Mon Sep 17 00:00:00 2001 From: jaseg Date: Wed, 14 Oct 2020 12:47:28 +0200 Subject: Move firmware into subdirectory --- .../arm_convolve_HWC_q15_fast_nonsquare.c | 265 +++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 fw/cdc-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c (limited to 'fw/cdc-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c') diff --git a/fw/cdc-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c b/fw/cdc-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c new file mode 100644 index 0000000..14d9130 --- /dev/null +++ b/fw/cdc-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c @@ -0,0 +1,265 @@ +/* + * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_HWC_q15_fast.c + * Description: Fast Q15 version of convolution + * + * $Date: 24. May 2018 + * $Revision: V.1.0.0 + * + * Target Processor: Cortex-M cores + * + * -------------------------------------------------------------------- */ + +#include "arm_math.h" +#include "arm_nnfunctions.h" + +/** + * @ingroup groupNN + */ + +/** + * @addtogroup NNConv + * @{ + */ + + /** + * @brief Fast Q15 convolution function (non-sqaure shape) + * @param[in] Im_in pointer to input tensor + * @param[in] dim_im_in_x input tensor dimention x + * @param[in] dim_im_in_y input tensor dimention y + * @param[in] ch_im_in number of input tensor channels + * @param[in] wt pointer to kernel weights + * @param[in] ch_im_out number of filters, i.e., output tensor channels + * @param[in] dim_kernel_x filter kernel size x + * @param[in] dim_kernel_y filter kernel size y + * @param[in] padding_x padding size x + * @param[in] padding_y padding size y + * @param[in] stride_x convolution stride x + * @param[in] stride_y convolution stride y + * @param[in] bias pointer to bias + * @param[in] bias_shift amount of left-shift for bias + * @param[in] out_shift amount of right-shift for output + * @param[in,out] Im_out pointer to output tensor + * @param[in] dim_im_out_x output tensor dimension x + * @param[in] dim_im_out_y output tensor dimension y + * @param[in,out] bufferA pointer to buffer space for input + * @param[in,out] bufferB pointer to buffer space for output + * @return The function returns either + * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. + * + * @details + * + * Buffer size: + * + * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel + * + * bufferB size: 0 + * + * Input dimension constraints: + * + * 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 + */ -- cgit