/* * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates * * 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_s16.c * Description: s16 version of convolution. * * $Date: 22 April 2024 * $Revision: V.4.0.0 * * Target : Arm(R) M-Profile Architecture * * -------------------------------------------------------------------- */ #include "arm_nnfunctions.h" #include "arm_nnsupportfunctions.h" /** * @ingroup Public */ /** * @addtogroup NNConv * @{ */ /* * Basic s16 convolution function. * * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels * are multiples of 4 or atleast greater than 4. * */ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx, const cmsis_nn_conv_params *conv_params, const cmsis_nn_per_channel_quant_params *quant_params, const cmsis_nn_dims *input_dims, const int16_t *input_data, const cmsis_nn_dims *filter_dims, const int8_t *filter_data, const cmsis_nn_dims *bias_dims, const cmsis_nn_bias_data *bias_data, const cmsis_nn_dims *output_dims, int16_t *output_data) { (void)bias_dims; if (ctx->buf == NULL) { return ARM_CMSIS_NN_ARG_ERROR; } int16_t *buffer_a = (int16_t *)ctx->buf; const int32_t input_batches = input_dims->n; const int32_t input_x = input_dims->w; const int32_t input_y = input_dims->h; const int32_t input_ch = input_dims->c; const int32_t kernel_x = filter_dims->w; const int32_t kernel_y = filter_dims->h; const int32_t output_x = output_dims->w; const int32_t output_y = output_dims->h; const int32_t output_ch = output_dims->c; const int32_t rhs_cols = input_ch * kernel_y * kernel_x; const int32_t dilation_x = conv_params->dilation.w; const int32_t dilation_y = conv_params->dilation.h; const int32_t pad_x = conv_params->padding.w; const int32_t pad_y = conv_params->padding.h; const int32_t stride_x = conv_params->stride.w; const int32_t stride_y = conv_params->stride.h; const int32_t out_activation_min = conv_params->activation.min; const int32_t out_activation_max = conv_params->activation.max; int32_t *output_mult = quant_params->multiplier; int32_t *output_shift = quant_params->shift; #if defined(ARM_MATH_MVEI) const int32_t rhs_rows = output_dims->c; #endif for (int i_batch = 0; i_batch < input_batches; i_batch++) { int16_t *im2col = buffer_a; int16_t *out = output_data; int32_t lhs_rows = 0; /* This part implements the im2col function */ for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++) { for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++) { const int32_t base_idx_x = stride_x * i_out_x - pad_x; const int32_t base_idx_y = stride_y * i_out_y - pad_y; for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++) { for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++) { const int32_t k_y = base_idx_y + dilation_y * i_ker_y; const int32_t k_x = base_idx_x + dilation_x * i_ker_x; if (k_y < 0 || k_y >= input_y || k_x < 0 || k_x >= input_x) { /* Filling 0 for out-of-bound paddings */ arm_memset_s8((int8_t *)im2col, 0, sizeof(int16_t) * (uint32_t)input_ch); } else { arm_memcpy_s8((int8_t *)im2col, (const int8_t *)(input_data + (k_y * input_x + k_x) * input_ch), (uint32_t)input_ch * sizeof(int16_t)); } im2col += input_ch; } } lhs_rows++; #if defined(ARM_MATH_MVEI) /* Computation is filed for every 4 columns */ if (lhs_rows == 4) { arm_nn_mat_mult_nt_t_s16(buffer_a, filter_data, bias_data, out, output_mult, output_shift, lhs_rows, rhs_rows, rhs_cols, out_activation_min, out_activation_max); out += lhs_rows * output_ch; lhs_rows = 0; im2col = buffer_a; } #else /* Computation is filed for every 2 columns */ if (lhs_rows == 2) { out = arm_nn_mat_mult_kernel_s16(filter_data, buffer_a, output_ch, output_shift, output_mult, out_activation_min, out_activation_max, rhs_cols, bias_data, out); /* Counter reset */ im2col = buffer_a; lhs_rows = 0; } #endif } if (out == NULL) { return ARM_CMSIS_NN_NO_IMPL_ERROR; } } /* Handle left over columns */ if (lhs_rows != 0) { #if defined(ARM_MATH_MVEI) arm_nn_mat_mult_nt_t_s16(buffer_a, filter_data, bias_data, out, output_mult, output_shift, lhs_rows, rhs_rows, rhs_cols, out_activation_min, out_activation_max); out += lhs_rows * rhs_rows; lhs_rows = 0; im2col = buffer_a; #else // #if defined(ARM_MATH_MVEI) const int64_t *bias_s64 = (const int64_t *)bias_data->data; const int32_t *bias_s32 = (const int32_t *)bias_data->data; const bool is_int32_bias = bias_data->is_int32_bias; const int8_t *ker_a = filter_data; int i; for (i = 0; i < output_ch; i++) { /* Init the accumulator*/ int32_t sum = 0; /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */ const int16_t *ip_as_col = buffer_a; #if defined(ARM_MATH_DSP) /* 4 multiply and accumulates are done in one loop. */ int32_t col_count = rhs_cols >> 2; while (col_count) { int32_t ker_a1, ker_a2; int32_t ip_b1, ip_b2; ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2); ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col); sum = SMLAD(ker_a1, ip_b1, sum); ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col); sum = SMLAD(ker_a2, ip_b2, sum); col_count--; } /* Handle left over mac */ col_count = rhs_cols & 0x3; #else uint16_t col_count = rhs_cols; #endif while (col_count) { int8_t ker_a1 = *ker_a++; int16_t ip_b1 = *ip_as_col++; sum += ker_a1 * ip_b1; col_count--; } if (is_int32_bias) { if (bias_s32) { sum += bias_s32[i]; } sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]); } else { int64_t acc_64 = sum; if (bias_s64) { acc_64 += bias_s64[i]; } int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]); sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]); } sum = MAX(sum, out_activation_min); sum = MIN(sum, out_activation_max); *out++ = (int16_t)sum; } lhs_rows = 0; #endif // #if defined(ARM_MATH_MVEI) } /* Advance to the next batch */ input_data += (input_x * input_y * input_ch); output_data += (output_x * output_y * output_ch); } /* Return to application */ return ARM_CMSIS_NN_SUCCESS; } /** * @} end of NNConv group */