COMPMID-697 - Rework GEMMLowp interface on OpenCL

Reworked the interface of GemmLowp in order to make easy the integration
in Android NN

- Added support for different output stage
- Added validation for both matrix multiplication and output stage
- Added bounded relu support in the output stage
- Added in32_t bias support
- Added optimized path for vector by matrix case

This rework is required for:
- Convolution quantized
- Fully connected quantized

Change-Id: I512283d406099cf8c614dd89d0a97ed411143afc
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110625
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>

1 files changed, 8 insertions, 2 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 92c911c370..da5ac22fdc 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -133,7 +133,10 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b,
     if(_a_offset != 0)
     {
         TensorShape shape_vector_sum_col = b->info()->tensor_shape();
-        shape_vector_sum_col.remove_dimension(1);
+        if(b->info()->num_dimensions() > 1)
+        {
+            shape_vector_sum_col.remove_dimension(1);
+        }
         TensorInfo info_vector_sum_col(shape_vector_sum_col, 1, DataType::S32);
         _vector_sum_col.allocator()->init(info_vector_sum_col);
         _memory_group.manage(&_vector_sum_col);
@@ -147,7 +150,10 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b,
     {
         TensorShape shape_vector_sum_row = a->info()->tensor_shape();
         shape_vector_sum_row.set(Window::DimX, a->info()->dimension(1));
-        shape_vector_sum_row.remove_dimension(1);
+        if(a->info()->num_dimensions() > 1)
+        {
+            shape_vector_sum_row.remove_dimension(1);
+        }
         TensorInfo info_vector_sum_row(shape_vector_sum_row, 1, DataType::S32);
         _vector_sum_row.allocator()->init(info_vector_sum_row);
         _memory_group.manage(&_vector_sum_row);