COMPMID-481: Add AArch64 GEMM

Change-Id: I34f94f99cb05f0eabafee13c5e623ee779b72360
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83741
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 8 insertions, 2 deletions

diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index dd2df727e9..fcaf4ef42b 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -26,6 +26,7 @@
 
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/Globals.h"
 #include "tests/IAccessor.h"
@@ -39,6 +40,8 @@
 
 namespace arm_compute
 {
+class NEConvolutionLayer;
+
 namespace test
 {
 namespace validation
@@ -85,6 +88,8 @@ protected:
         {
             // Check if its a "fully connected" convolution
             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
+            const bool is_optimised                   = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
+
             reshaped_weights_shape.collapse(3);
 
             if(bias_shape.total_size() > 0)
@@ -92,7 +97,7 @@ protected:
                 reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1);
             }
 
-            if(is_fully_connected_convolution)
+            if(is_fully_connected_convolution || is_optimised)
             {
                 const size_t shape_x = reshaped_weights_shape.x();
                 reshaped_weights_shape.set(0, reshaped_weights_shape.y());
@@ -138,6 +143,7 @@ protected:
         if(!reshape_weights)
         {
             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
+            const bool is_optimised                   = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
 
             TensorShape     tmp_weights_shape(weights_shape);
             SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position);
@@ -149,7 +155,7 @@ protected:
 
             tmp_weights = linearise_weights(tmp_weights, &tmp_bias);
 
-            if(!is_fully_connected_convolution)
+            if(!is_fully_connected_convolution && !is_optimised)
             {
                 // Transpose with interleave
                 const int interleave_size = 16 / tmp_weights.element_size();