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TFLM Cortex-A NEON Conv

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Use neon to accelerate the conv op, and the output results are the same.

Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
This commit is contained in:
xinhaiteng 2023-11-24 17:56:37 +08:00 • committed by Xiang Xiao
parent 71cbcb2618
commit 754d9e39a8
1 changed files with 94 additions and 0 deletions
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94
mlearning/tflite-micro/tflite-micro.patch
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@ -12,3 +12,97 @@ index 7638d912..3261be56 100644
namespace tflite {
namespace tflm_signal {
diff --git a/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h b/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h
index eac00576..abfdea8c 100644
--- a/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h
+++ b/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h
@@ -18,6 +18,9 @@ limitations under the License.
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
+#ifdef USE_NEON
+#include <arm_neon.h>
+#endif
namespace tflite {
namespace reference_integer_ops {
@@ -133,6 +136,79 @@ inline void ConvPerChannel(
}
}
}
+#ifdef USE_NEON
+ for (int batch = 0; batch < batches; ++batch) {
+ for (int out_y = 0; out_y < output_height; ++out_y) {
+ int in_y_origin = (out_y * stride_height) - pad_height;
+ for (int out_x = 0; out_x < output_width; ++out_x) {
+ int in_x_origin = (out_x * stride_width) - pad_width;
+ int filter_start_offset = 0;
+ for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
+ auto group = out_channel / filters_per_group;
+ int32_t acc = 0;
+ int8x8_t input_v = vdup_n_s8(0);
+ int8x8_t filter_v = vdup_n_s8(0);
+ int16x8_t mid_mul = vdupq_n_s16(0);
+ int32x4_t res_v = vdupq_n_s32(0);
+ int32x4_t filter_offset_v = vdupq_n_s32(0);
+ int input_offset_temp = 0;
+ for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+ int in_y = in_y_origin + dilation_height_factor * filter_y;
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+ int in_x = in_x_origin + dilation_width_factor * filter_x;
+ const bool is_point_inside_image = (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height);
+ if (!is_point_inside_image)
+ continue;
+ int input_start_offset = ((batch * input_height + in_y) * input_width + in_x) * input_depth + group * filter_input_depth;
+ int in_channel = 0;
+ for (; in_channel < (filter_input_depth & -8); in_channel += 8) {
+ input_v = vld1_s8(input_data + input_start_offset);
+ input_start_offset += 8;
+ filter_v = vld1_s8(filter_data + filter_start_offset);
+ filter_start_offset += 8;
+
+ mid_mul = vmovl_s8(filter_v);
+ filter_offset_v = vaddw_s16(filter_offset_v, vget_low_s16(mid_mul));
+ filter_offset_v = vaddw_s16(filter_offset_v, vget_high_s16(mid_mul));
+ mid_mul = vmull_s8(input_v, filter_v);
+ res_v = vaddw_s16(res_v, vget_low_s16(mid_mul));
+ res_v = vaddw_s16(res_v, vget_high_s16(mid_mul));
+
+ }
+
+ for (; in_channel < filter_input_depth; ++in_channel) {
+ acc += (input_data[input_start_offset] + input_offset) * filter_data[filter_start_offset];
+ ++input_start_offset;
+ ++filter_start_offset;
+ }
+ }
+ }
+ acc += vgetq_lane_s32(res_v, 0);
+ acc += vgetq_lane_s32(res_v, 1);
+ acc += vgetq_lane_s32(res_v, 2);
+ acc += vgetq_lane_s32(res_v, 3);
+ input_offset_temp += vgetq_lane_s32(filter_offset_v, 0);
+ input_offset_temp += vgetq_lane_s32(filter_offset_v, 1);
+ input_offset_temp += vgetq_lane_s32(filter_offset_v, 2);
+ input_offset_temp += vgetq_lane_s32(filter_offset_v, 3);
+ acc += input_offset_temp * input_offset;
+
+ if (bias_data)
+ {
+ acc += bias_data[out_channel];
+ }
+ acc = MultiplyByQuantizedMultiplier(
+ acc, output_multiplier[out_channel], output_shift[out_channel]);
+ acc += output_offset;
+ acc = std::max(acc, output_activation_min);
+ acc = std::min(acc, output_activation_max);
+ output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
+ static_cast<int8_t>(acc);
+ }
+ }
+ }
+ }
+#endif
}