static void ult_nn_convolution_fixedpoint_comp_both_initialize_matrices(
int16_t* input,
int16_t* output,
int32_t* biases,
int16_t* kernel,
int16_t* input_ref,
int16_t* output_ref,
int32_t* biases_ref,
int16_t* kernel_ref,
uint_least32_t num_output_feature_maps,
uint_least32_t num_input_feature_maps,
uint_least32_t output_feature_map_width,
uint_least32_t output_feature_map_height,
uint_least32_t input_feature_map_width,
uint_least32_t input_feature_map_height,
uint_least32_t kernel_width,
uint_least32_t kernel_height,
uint_least32_t center_x,
uint_least32_t center_y
)
{
uint_least32_t input_size = input_feature_map_width * input_feature_map_height * num_input_feature_maps * sizeof(int16_t);
int16_t * inputT = (int16_t*)_mm_malloc(input_size, 4096);
uint_least32_t kernel_size = num_input_feature_maps * num_output_feature_maps * kernel_width * kernel_height * sizeof(int16_t);
int16_t * weightT = (int16_t*)_mm_malloc(kernel_size, 4096);
uint32_t IFMBlock = 16;
if (num_input_feature_maps == 4) IFMBlock = 4;
uint32_t OFMBlock = 32;
uint32_t OFMpBlock = 2;
if (num_output_feature_maps == 3072 && num_input_feature_maps == 1024)
{
IFMBlock = 16;
OFMBlock = 384;
}
for (uint_least32_t input_map = 0; input_map < num_input_feature_maps; input_map++)
{
uint_least32_t element = 0;
int16_t value = input_map * 0x0100;
for (uint_least32_t row = 0; row < input_feature_map_height; row++)
{
for (uint_least32_t column = 0; column < input_feature_map_width; column++)
{
uint_least32_t offset;
//ult_nn_convolution_fixedpoint_comp_optimized_set_input_value(inputT, input_feature_map_width, num_input_feature_maps, column, row, input_map, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_input_value(inputT, input_feature_map_width, input_feature_map_height, column, row, input_map, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_input_value(input_ref, input_feature_map_width, input_feature_map_height, column, row, input_map, value, offset);
value++;
}
}
}
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
for (uint_least32_t input_map = 0; input_map < num_input_feature_maps; input_map++)
{
uint_least32_t element = 0;
int16_t value = input_map * 0x0100 + outmapa * 0x2000;
for (uint_least32_t row = 0; row < kernel_height; row++)
{
for (uint_least32_t column = 0; column < kernel_width; column++)
{
element++;
uint_least32_t offset;
//ult_nn_convolution_fixedpoint_comp_optimized_set_kernel_value
ult_nn_merge_convolution_fixedpoint_naive_set_kernel_value(kernel, kernel_width, kernel_height, num_input_feature_maps, column, row, input_map, outmapa, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_kernel_value(kernel_ref, kernel_width, kernel_height, num_input_feature_maps, column, row, input_map, outmapa, value, offset);
value++;
}
}
}
}
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
for (uint_least32_t row = 0; row < output_feature_map_height + 2 * center_y; row++)
{
for (uint_least32_t column = 0; column < output_feature_map_width + 2 * center_x; column++)
{
uint32_t index = column + row * (output_feature_map_width + 2 * center_x) + outmapa * (output_feature_map_width + 2 * center_x) * (output_feature_map_height + 2 * center_y);
output[index] = 0;
output_ref[index] = 0;
}
}
}
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
biases[outmapa] = outmapa;
biases_ref[outmapa] = outmapa;
}
//prepare right input layout for naive implementation
for (size_t y = 0; y < input_feature_map_height; y++)
{
for (size_t x = 0; x < input_feature_map_width; x++)
{
for (size_t z = 0; z < num_input_feature_maps; z++)
{
input[z + x * num_input_feature_maps + y * num_input_feature_maps * input_feature_map_width]
= inputT[z * input_feature_map_width * input_feature_map_height + y * input_feature_map_height + x];
}
}
}
_mm_free(inputT);
}
static void ult_nn_merge_convolution_fixedpoint_both_initialize_matrices(
int16_t* input,
int16_t* output,
int32_t* biases,
int16_t* kernel,
int16_t* input_ref,
int16_t* output_ref,
int32_t* biases_ref,
int16_t* kernel_ref,
uint_least32_t num_output_feature_maps,
uint_least32_t num_input_feature_maps,
uint_least32_t output_feature_map_width,
uint_least32_t output_feature_map_height,
uint_least32_t input_feature_map_width,
uint_least32_t input_feature_map_height,
uint_least32_t kernel_width,
uint_least32_t kernel_height,
uint_least32_t center_x,
uint_least32_t center_y
)
{
uint_least32_t input_size = input_feature_map_width * input_feature_map_height * num_input_feature_maps * sizeof(int16_t);
int16_t * inputT = (int16_t*)_mm_malloc(input_size, 4096);
uint_least32_t kernel_size = num_input_feature_maps * num_output_feature_maps * kernel_width * kernel_height * sizeof(int16_t);
int16_t * weightT = (int16_t*)_mm_malloc(kernel_size, 4096);
uint32_t IFMBlock = 8;
if (num_input_feature_maps == 4) IFMBlock = 4;
uint32_t OFMBlock = 32;
uint32_t OFMpBlock = 2;
if (num_output_feature_maps == 3072 && num_input_feature_maps == 1024)
{
IFMBlock = 8;
OFMBlock = 384;
}
for (uint_least32_t input_map = 0; input_map < num_input_feature_maps; input_map++)
{
uint_least32_t element = 0;
int16_t value = input_map * 0x0100;
for (uint_least32_t row = 0; row < input_feature_map_height; row++)
{
for (uint_least32_t column = 0; column < input_feature_map_width; column++)
{
uint_least32_t offset;
//ult_nn_convolution_optimized_set_input_value(inputT, input_feature_map_width, num_input_feature_maps, column, row, input_map, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_input_value(inputT, input_feature_map_width, input_feature_map_height, column, row, input_map, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_input_value(input_ref, input_feature_map_width, input_feature_map_height, column, row, input_map, value, offset);
value++;
}
}
}
int16_t value = 0;
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
for (uint_least32_t input_map = 0; input_map < num_input_feature_maps; input_map++)
{
//uint_least32_t element = 0;
//int16_t value = input_map * 0x0100 + outmapa * 0x2000;
for (uint_least32_t row = 0; row < kernel_height; row++)
{
for (uint_least32_t column = 0; column < kernel_width; column++)
{
//element++;
uint_least32_t offset;
//ult_nn_convolution_optimized_set_kernel_value
ult_nn_merge_convolution_fixedpoint_naive_set_kernel_value(kernel, kernel_width, kernel_height, num_input_feature_maps, column, row, input_map, outmapa, value, offset);
ult_nn_merge_convolution_fixedpoint_naive_set_kernel_value(kernel_ref, kernel_width, kernel_height, num_input_feature_maps, column, row, input_map, outmapa, value, offset);
value++;
}
}
}
}
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
for (uint_least32_t row = 0; row < output_feature_map_height + 2 * center_y; row++)
{
for (uint_least32_t column = 0; column < output_feature_map_width + 2 * center_x; column++)
{
uint32_t index = column + row * (output_feature_map_width + 2 * center_x) + outmapa * (output_feature_map_width + 2 * center_x) * (output_feature_map_height + 2 * center_y);
output[index] = 0;
output_ref[index] = 0;
}
}
}
for (uint_least32_t outmapa = 0; outmapa < num_output_feature_maps; outmapa++)
{
biases[outmapa] = outmapa;
biases_ref[outmapa] = outmapa;
}
//prepare right input layout for zxy
for (size_t y = 0; y < input_feature_map_height; y++)
{
for (size_t x = 0; x < input_feature_map_width; x++)
{
for (size_t z = 0; z < num_input_feature_maps; z++)
{
input[z + x * num_input_feature_maps + y * num_input_feature_maps * input_feature_map_width]
= inputT[z * input_feature_map_width * input_feature_map_height + y * input_feature_map_height + x];
}
}
}
//prepare right input layout for naive implementation
/*for (uint32_t i = 0; i < num_input_feature_maps / IFMBlock; i++)
for (uint32_t j = 0; j < input_feature_map_width * input_feature_map_height; j++)
for (uint32_t n = 0; n < IFMBlock; n++)
input[n + j * IFMBlock + i * input_feature_map_width * input_feature_map_height * IFMBlock]
= inputT[n * input_feature_map_width * input_feature_map_height + j + i * input_feature_map_width * input_feature_map_height * IFMBlock];
const uint32_t ItrIn = num_input_feature_maps / 2;
const uint32_t ItrOut = num_output_feature_maps / OFMBlock;
for (uint32_t k = 0; k < ItrOut; k++)
for (uint32_t i = 0; i < kernel_width * kernel_height; i++)
for (uint32_t j = 0; j < ItrIn; j++)
for (uint32_t n = 0; n < OFMBlock; n++)
for (uint32_t m = 0; m < 2; m++)
weightT[m + 2 * n + 2 * OFMBlock * j + i * 2 * OFMBlock * ItrIn + k * 2 * OFMBlock * ItrIn * kernel_width * kernel_height]
= kernel[m * kernel_width * kernel_height + n * num_input_feature_maps * kernel_width * kernel_height + 2 * j * kernel_width * kernel_height + k * OFMBlock * num_input_feature_maps * kernel_width * kernel_height + i];*/
_mm_free(inputT);
//_mm_free(weightT);
}
