/* * Copyright (C) 2010-2021 Arm Limited 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_fast_s16.c * Description: Optimized s16 version of convolution. * * $Date: 12 August 2021 * $Revision: V.1.1.0 * * Target Processor: Cortex-M cores * * -------------------------------------------------------------------- */ #include "arm_nnfunctions.h" #include "arm_nnsupportfunctions.h" /** * @ingroup groupNN */ /** * @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_status arm_convolve_fast_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 q15_t *input_data, const cmsis_nn_dims *filter_dims, const q7_t *filter_data, const cmsis_nn_dims *bias_dims, const int64_t *bias_data, const cmsis_nn_dims *output_dims, q15_t *output_data) { (void)bias_dims; if (filter_dims->w * filter_dims->h * input_dims->c >= 512) { return ARM_MATH_SIZE_MISMATCH; } if (ctx->buf == NULL && arm_convolve_s8_get_buffer_size(input_dims, filter_dims) > 0) { return ARM_MATH_ARGUMENT_ERROR; } q15_t *buffer_a = (q15_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 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 int16_t out_activation_min = conv_params->activation.min; const int16_t out_activation_max = conv_params->activation.max; int32_t *output_mult = quant_params->multiplier; int32_t *output_shift = quant_params->shift; for (int i_batch = 0; i_batch < input_batches; i_batch++) { #if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) /* Generate two columns from the input tensor a GEMM computation */ q15_t *two_column_buf = buffer_a; q15_t *out = output_data; /* 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++) { for (int32_t i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y; i_ker_y++) { for (int32_t i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x; i_ker_x++) { if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) { /* Filling 0 for out-of-bound paddings */ arm_memset_q7((q7_t *)two_column_buf, 0, sizeof(q15_t) * input_ch); } else { arm_memcpy_q7((q7_t *)two_column_buf, (const q7_t *)(input_data + (i_ker_y * input_x + i_ker_x) * input_ch), input_ch * sizeof(q15_t)); } two_column_buf += input_ch; } } /* Computation is filed for every 2 columns */ if (two_column_buf == buffer_a + 2 * input_ch * kernel_y * kernel_x) { out = arm_nn_mat_mult_kernel_s16(filter_data, buffer_a, output_ch, output_shift, output_mult, out_activation_min, out_activation_max, (input_ch * kernel_y * kernel_x), bias_data, out); /* Counter reset */ two_column_buf = buffer_a; } } } /* Left-over because odd number of output pixels */ if (two_column_buf != buffer_a) { const q7_t *ker_a = filter_data; int i; for (i = 0; i < output_ch; i++) { /* Init the accumulator*/ q31_t sum = 0; /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */ const q15_t *ip_as_col = buffer_a; /* 4 multiply and accumulates are done in one loop. */ uint16_t col_count = (input_ch * kernel_y * kernel_x) >> 2; while (col_count) { q31_t ker_a1, ker_a2; q31_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 = input_ch * kernel_y * kernel_x & 0x3; while (col_count) { q7_t ker_a1 = *ker_a++; q15_t ip_b1 = *ip_as_col++; sum += ker_a1 * ip_b1; col_count--; } if (bias_data) { q31_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]); q63_t acc_64 = sum + bias_data[i]; sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]); } else { sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]); } sum = MAX(sum, out_activation_min); sum = MIN(sum, out_activation_max); *out++ = (q15_t)sum; } } #else (void)input_data; (void)output_data; (void)bias_data; (void)filter_data; (void)buffer_a; (void)kernel_x; (void)kernel_y; (void)pad_x; (void)pad_y; (void)stride_x; (void)stride_y; (void)out_activation_min; (void)out_activation_max; (void)output_mult; (void)output_shift; return ARM_MATH_ARGUMENT_ERROR; #endif /* 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_MATH_SUCCESS; } int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims) { #if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t); #else (void)input_dims; (void)filter_dims; return 0; #endif } /** * @} end of NNConv group */