diff options
Diffstat (limited to 'Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s16.c')
| -rw-r--r-- | Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s16.c | 292 |
1 files changed, 292 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s16.c b/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s16.c new file mode 100644 index 0000000..42e4bbd --- /dev/null +++ b/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s16.c @@ -0,0 +1,292 @@ +/* + * Copyright (C) 2022 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_depthwise_conv_s16.c + * Description: s16 version of depthwise convolution. + * + * $Date: 26. Jan 2022 + * $Revision: V.1.0.0 + * + * Target Processor: Cortex-M CPUs + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupNN + */ + +/** + * @addtogroup NNConv + * @{ + */ + +static void __attribute__((unused)) depthwise_conv_s16_mult_4_s16(const int16_t *input, + const int32_t input_x, + const int32_t input_y, + const int32_t input_ch, + const int8_t *kernel, + const int32_t output_ch, + const int32_t ch_mult, + const int32_t kernel_x, + const int32_t kernel_y, + const int32_t pad_x, + const int32_t pad_y, + const int32_t stride_x, + const int32_t stride_y, + const int64_t *bias, + int16_t *output, + const int32_t *output_shift, + const int32_t *output_mult, + const int32_t output_x, + const int32_t output_y, + const int32_t output_activation_min, + const int32_t output_activation_max) +{ + for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) + { + for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) + { + for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch; + ++in_ch, out_ch += ch_mult) + { + for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4) + { + int32_t out_buff32[4] = {REDUCE_MULTIPLIER(output_mult[out_ch + 0 + mult_tile]), + REDUCE_MULTIPLIER(output_mult[out_ch + 1 + mult_tile]), + REDUCE_MULTIPLIER(output_mult[out_ch + 2 + mult_tile]), + REDUCE_MULTIPLIER(output_mult[out_ch + 3 + mult_tile])}; + + int64_t out_buff[4] = {0, 0, 0, 0}; + + if (bias) + { + out_buff[0] = bias[out_ch + 0 + mult_tile]; + out_buff[1] = bias[out_ch + 1 + mult_tile]; + out_buff[2] = bias[out_ch + 2 + mult_tile]; + out_buff[3] = bias[out_ch + 3 + mult_tile]; + } + + for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h) + { + int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch; + int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch; +#if defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050) +#pragma clang loop unroll(disable) +#endif + for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w); + ++ker_w, ker_idx += output_ch) + { + // TODO: Unroll of 4 with 64 bit accumulator will probably result in too much register + // spills. Try with unroll of 2 when enabling this. + int32_t in_val = input[in_idx + ker_w * input_ch]; + out_buff[0] += in_val * kernel[ker_idx + 0 + mult_tile]; + out_buff[1] += in_val * kernel[ker_idx + 1 + mult_tile]; + out_buff[2] += in_val * kernel[ker_idx + 2 + mult_tile]; + out_buff[3] += in_val * kernel[ker_idx + 3 + mult_tile]; + } + } + + out_buff32[0] = + arm_nn_requantize_s64(out_buff[0], out_buff32[0], output_shift[out_ch + 0 + mult_tile]); + out_buff32[1] = + arm_nn_requantize_s64(out_buff[1], out_buff32[1], output_shift[out_ch + 1 + mult_tile]); + out_buff32[2] = + arm_nn_requantize_s64(out_buff[2], out_buff32[2], output_shift[out_ch + 2 + mult_tile]); + out_buff32[3] = + arm_nn_requantize_s64(out_buff[3], out_buff32[3], output_shift[out_ch + 3 + mult_tile]); + + out_buff32[0] = MIN(MAX(out_buff32[0], output_activation_min), output_activation_max); + out_buff32[1] = MIN(MAX(out_buff32[1], output_activation_min), output_activation_max); + out_buff32[2] = MIN(MAX(out_buff32[2], output_activation_min), output_activation_max); + out_buff32[3] = MIN(MAX(out_buff32[3], output_activation_min), output_activation_max); + + output[out_idx++] = (int16_t)out_buff32[0]; + output[out_idx++] = (int16_t)out_buff32[1]; + output[out_idx++] = (int16_t)out_buff32[2]; + output[out_idx++] = (int16_t)out_buff32[3]; + } + } + } + } +} + +static void depthwise_conv_s16_generic_s16(const int16_t *input, + const uint16_t input_batches, + const uint16_t input_x, + const uint16_t input_y, + const uint16_t input_ch, + const int8_t *kernel, + const uint16_t ch_mult, + const uint16_t kernel_x, + const uint16_t kernel_y, + const uint16_t pad_x, + const uint16_t pad_y, + const uint16_t stride_x, + const uint16_t stride_y, + const int64_t *bias, + int16_t *output, + const int32_t *output_shift, + const int32_t *output_mult, + const uint16_t output_x, + const uint16_t output_y, + const int32_t output_activation_min, + const int32_t output_activation_max, + const uint16_t dilation_x, + const uint16_t dilation_y) + +{ + for (int i_batch = 0; i_batch < input_batches; i_batch++) + { + for (int i_out_y = 0; i_out_y < output_y; i_out_y++) + { + const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; + for (int i_out_x = 0; i_out_x < output_x; i_out_x++) + { + const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; + for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) + { + for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) + { + const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult; + + const q31_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[idx_out_ch]); + int64_t acc_0 = 0; + + int ker_y_start; + int ker_x_start; + int ker_y_end; + int ker_x_end; + + if (dilation_x > 1) + { + const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x; + ker_x_start = MAX(0, start_x_max); + const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x; + ker_x_end = MIN(kernel_x, end_min_x); + } + else + { + ker_x_start = MAX(0, -base_idx_x); + ker_x_end = MIN(kernel_x, input_x - base_idx_x); + } + + if (dilation_y > 1) + { + const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y; + ker_y_start = MAX(0, start_y_max); + const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y; + ker_y_end = MIN(kernel_y, end_min_y); + } + else + { + ker_y_start = MAX(0, -base_idx_y); + ker_y_end = MIN(kernel_y, input_y - base_idx_y); + } + + if (bias) + { + acc_0 = bias[idx_out_ch]; + } + + for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) + { + const int32_t idx_y = base_idx_y + dilation_y * i_ker_y; + for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) + { + const int32_t idx_x = base_idx_x + dilation_x * i_ker_x; + int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch; + int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch; + + acc_0 += input[idx_0] * kernel[ker_idx_0]; + } + } + + /* Requantize and clamp output to provided range */ + int32_t result = arm_nn_requantize_s64(acc_0, reduced_multiplier, output_shift[idx_out_ch]); + result = MAX(result, output_activation_min); + result = MIN(result, output_activation_max); + *output++ = (int16_t)result; + } + } + } + } + /* Advance to the next batch */ + input += (input_x * input_y * input_ch); + } +} + +/* + * Basic s16 depthwise convolution function. + * + * Refer header file for details. + * + */ +arm_status arm_depthwise_conv_s16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params *dw_conv_params, + const cmsis_nn_per_channel_quant_params *quant_params, + const cmsis_nn_dims *input_dims, + const q15_t *input, + const cmsis_nn_dims *filter_dims, + const q7_t *kernel, + const cmsis_nn_dims *bias_dims, + const int64_t *bias, + const cmsis_nn_dims *output_dims, + q15_t *output) +{ + const uint16_t dilation_x = dw_conv_params->dilation.w; + const uint16_t dilation_y = dw_conv_params->dilation.h; + + (void)bias_dims; + (void)ctx; + + depthwise_conv_s16_generic_s16(input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + dw_conv_params->ch_mult, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + quant_params->shift, + quant_params->multiplier, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max, + dilation_x, + dilation_y); + + /* Return to application */ + return ARM_MATH_SUCCESS; +} + +/** + * @} end of NNConv group + */ |