[luci-interpreter] Support Conv2dS8 CMSIS-NN optimized kernel (Samsun…

…g#7890)

This commit introduces Conv2dS8 CMSIS-NN optimized kernel support.

ONE-DCO-1.0-Signed-off-by: Vyacheslav Bazhenov <[email protected]>

compiler/luci-interpreter/pal/cmsisnn/PALConv2d.h

@@ -19,6 +19,8 @@
 
 #include <tensorflow/lite/kernels/internal/reference/conv.h>
 #include <tensorflow/lite/kernels/internal/reference/integer_ops/conv.h>
+#include <arm_nn_types.h>
+#include <arm_nnfunctions.h>
 
 namespace luci_interpreter_pal
 {
@@ -60,9 +62,82 @@ static inline void ConvPerChannel(const tflite::ConvParams &params, const int32_
 {
   (void)im2col_shape;
   (void)im2col_data;
-  tflite::reference_integer_ops::ConvPerChannel(params, mult, shifts, input_shape, input_data,
-                                                filter_shape, filter_data, bias_shape, bias_data,
-                                                output_shape, output_data);
+
+  cmsis_nn_conv_params conv_params;
+  conv_params.dilation.h = params.dilation_height_factor;
+  conv_params.dilation.w = params.dilation_width_factor;
+
+  if (conv_params.dilation.h == 1 && conv_params.dilation.w == 1)
+  {
+    conv_params.input_offset = params.input_offset;
+    conv_params.output_offset = params.output_offset;
+    conv_params.stride.h = params.stride_height;
+    conv_params.stride.w = params.stride_width;
+    conv_params.padding.h = params.padding_values.height;
+    conv_params.padding.w = params.padding_values.width;
+    conv_params.activation.min = params.quantized_activation_min;
+    conv_params.activation.max = params.quantized_activation_max;
+
+    cmsis_nn_per_channel_quant_params quant_params;
+    quant_params.multiplier = const_cast<int32_t *>(mult);
+    quant_params.shift = const_cast<int32_t *>(shifts);
+
+    assert(conv_params.activation.min <= conv_params.activation.max);
+    assert(input_shape.DimensionsCount() == 4);
+    assert(filter_shape.DimensionsCount() == 4);
+    assert(output_shape.DimensionsCount() == 4);
+    const int batch_size = tflite::MatchingDim(input_shape, 0, output_shape, 0);
+    const int input_depth = tflite::MatchingDim(input_shape, 3, filter_shape, 3);
+    const int output_depth = tflite::MatchingDim(filter_shape, 0, output_shape, 3);
+    if (bias_data)
+    {
+      assert(bias_shape.FlatSize() == output_depth);
+    }
+
+    cmsis_nn_dims input_dims;
+    input_dims.n = batch_size;
+    input_dims.h = input_shape.Dims(1);
+    input_dims.w = input_shape.Dims(2);
+    input_dims.c = input_depth;
+
+    cmsis_nn_dims filter_dims;
+    filter_dims.n = output_depth;
+    filter_dims.h = filter_shape.Dims(1);
+    filter_dims.w = filter_shape.Dims(2);
+    filter_dims.c = input_depth;
+
+    cmsis_nn_dims bias_dims;
+    bias_dims.n = 1;
+    bias_dims.h = 1;
+    bias_dims.w = 1;
+    bias_dims.c = output_depth;
+
+    cmsis_nn_dims output_dims;
+    output_dims.n = batch_size;
+    output_dims.h = output_shape.Dims(1);
+    output_dims.w = output_shape.Dims(2);
+    output_dims.c = output_depth;
+
+    const int32_t buf_size = arm_convolve_wrapper_s8_get_buffer_size(&conv_params, &input_dims,
+                                                                     &filter_dims, &output_dims);
+    auto buffer = std::make_unique<int8_t[]>(buf_size);
+    assert(buffer != nullptr);
+
+    cmsis_nn_context ctx;
+    ctx.buf = buffer.get();
+    ctx.size = buf_size;
+
+    auto res = arm_convolve_wrapper_s8(&ctx, &conv_params, &quant_params, &input_dims, input_data,
+                                       &filter_dims, filter_data, &bias_dims, bias_data,
+                                       &output_dims, output_data);
+    assert(res == ARM_MATH_SUCCESS);
+  }
+  else
+  {
+    tflite::reference_integer_ops::ConvPerChannel(params, mult, shifts, input_shape, input_data,
+                                                  filter_shape, filter_data, bias_shape, bias_data,
+                                                  output_shape, output_data);
+  }
 }
 
 } // namespace luci_interpreter_pal