COMPMID-436, COMPMID-437 - Port NEConvolutionLayer & NEFullyConnectedLayer to support 16 bit fixed point

Change-Id: I69edf2dac242f941bac95c8479d921e7be6abca7
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/79725
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

16 files changed, 78 insertions, 47 deletions

diff --git a/arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h b/arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h
index c0ecafcd39..1eed4e7a84 100644
--- a/arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h
+++ b/arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h
@@ -47,7 +47,7 @@ public:
     ~NEGEMMMatrixAccumulateBiasesKernel() = default;
     /** Set the accumulate buffer and the biases of the kernel.
      *
-     * @param[in, out] accum  The accumulate tensor to convert. Data type supported: QS8/F32
+     * @param[in, out] accum  The accumulate tensor to convert. Data type supported: QS8/QS16/F32
      * @param[in]      biases The shared biases tensor to append. It must be 1D Tensor. Data type supported: Same as @p input
      */
     void configure(ITensor *accum, const ITensor *biases);
diff --git a/arm_compute/core/NEON/kernels/NEIm2ColKernel.h b/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
index 9b8b98b388..87d7cc0a8b 100644
--- a/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
+++ b/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
@@ -73,7 +73,7 @@ public:
     /** Set the input and output of the kernel.
      *
      * @param[in]  input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
-     *                         while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QS8/F16/F32
+     *                         while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QS8/QS16/F16/F32
      * @param[out] output      The output tensor. Data types supported: Same as @p input
      * @param[in]  kernel_dims The kernel dimensions (width and height).
      * @param[in]  conv_info   Contains padding and stride information described in @ref PadStrideInfo.
diff --git a/arm_compute/core/NEON/kernels/NETransposeKernel.h b/arm_compute/core/NEON/kernels/NETransposeKernel.h
index ac9449ff92..2f757f18eb 100644
--- a/arm_compute/core/NEON/kernels/NETransposeKernel.h
+++ b/arm_compute/core/NEON/kernels/NETransposeKernel.h
@@ -53,7 +53,7 @@ public:
 
     /** Initialise the kernel's input and output.
      *
-     * @param[in]  input  Input tensor. Data types supported: U8/S8/QS8/U16/S16/F16/U32/S32/F32
+     * @param[in]  input  Input tensor. Data types supported: U8/S8/QS8/U16/S16/QS16/F16/U32/S32/F32
      * @param[out] output Output tensor. Data type supported: Same as @p input
      */
     void configure(const ITensor *input, ITensor *output);
@@ -64,7 +64,7 @@ public:
 private:
     /** Common signature for all the transpose functions
      *
-     * @param[in]  input  An input tensor. Data types supported: U8/S8/QS8/U16/S16/F16/U32/S32/F32
+     * @param[in]  input  An input tensor. Data types supported: U8/S8/QS8/U16/S16/QS16/F16/U32/S32/F32
      * @param[out] output The output tensor. Data type supported: same as @p input
      * @param[in]  window Region on which to execute the kernel.
      */
diff --git a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
index cad2d00b1f..6b76d19314 100644
--- a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
+++ b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
@@ -71,7 +71,7 @@ public:
     /** Set the input and output of the kernel.
      *
      * @param[in]  input  The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
-     *                    and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QS8/F32
+     *                    and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QS8/QS16/F32
      * @param[in]  bias   The shared biases tensor to append.  Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
      *                    dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input
      * @param[out] output The output tensor. Data types supported: Same as @p input
diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
index a8fff8d047..1bd7e6a95f 100644
--- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
@@ -51,7 +51,7 @@ public:
     NEConvolutionLayerReshapeWeights();
     /** Set the input and output tensors.
      *
-     * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/F32.
+     * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QS16/F32.
      * @param[in]  biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
      * @param[out] output       Destination tensor. Data types supported: Same as @p weights.
      * @param[in]  transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
@@ -84,7 +84,7 @@ public:
      *
      * @param[in]  input        Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
      *                          while every optional dimension from 4 and above represent a batch of inputs.
-     *                          Data types supported: QS8/F32.
+     *                          Data types supported: QS8/QS16/F32.
      * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in]  biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
      * @param[out] output       Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
index 33ec4ef721..af571d1057 100644
--- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
@@ -50,7 +50,7 @@ public:
     NEFullyConnectedLayerReshapeWeights();
     /** Set the input and output tensors.
      *
-     * @param[in]  input               Weights tensor. The weights must be 2 dimensional. Data types supported: QS8/F32.
+     * @param[in]  input               Weights tensor. The weights must be 2 dimensional. Data types supported: QS8/QS16/F32.
      * @param[out] output              Destination tensor. Data type supported: Same as @p input.
      * @param[in]  transpose_weights   True if the weights must be transposed. Data types supported: Same as @p weights.
      * @param[in]  is_batched_fc_layer True if it is a batched fully connected layer
@@ -84,7 +84,7 @@ public:
     NEFullyConnectedLayer();
     /** Set the input and output tensors.
      *
-     * @param[in]  input                Source tensor. Data type supported: QS8/F32.
+     * @param[in]  input                Source tensor. Data type supported: QS8/QS16/F32.
      * @param[in]  weights              Weights tensor. The weights must be 2 dimensional. Data type supported: Same as @p input.
      * @param[in]  biases               Bias tensor. Can be nullptr. Data type supported:Same as @p input.
      * @param[out] output               Destination tensor. Data type supported: Same as @p input.
diff --git a/src/core/NEON/kernels/NECol2ImKernel.cpp b/src/core/NEON/kernels/NECol2ImKernel.cpp
index e9a73607e6..95a9364082 100644
--- a/src/core/NEON/kernels/NECol2ImKernel.cpp
+++ b/src/core/NEON/kernels/NECol2ImKernel.cpp
@@ -69,7 +69,8 @@ NECol2ImKernel::NECol2ImKernel()
 
 void NECol2ImKernel::configure(const ITensor *input, ITensor *output, std::pair<unsigned int, unsigned int> convolved_dims)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::QS16, DataType::U32, DataType::S32, DataType::F16,
+                                                  DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
 
     TensorShape output_shape = input->info()->tensor_shape();
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
index 7a3bae50c0..826a386557 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
@@ -45,9 +45,9 @@ NEGEMMMatrixAccumulateBiasesKernel::NEGEMMMatrixAccumulateBiasesKernel()
 
 void NEGEMMMatrixAccumulateBiasesKernel::configure(ITensor *accum, const ITensor *biases)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(biases, accum);
     ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() != 1);
 
     _biases = biases;
@@ -121,6 +121,21 @@ void NEGEMMMatrixAccumulateBiasesKernel::run(const Window &window)
             in0_out, in1);
             break;
         }
+        case DataType::QS16:
+        {
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                qint16x8x2_t       accum  = vld2q_s16(reinterpret_cast<const qint16_t *>(in0_out.ptr()));
+                const qint16x8x2_t biases = vld2q_s16(reinterpret_cast<const qint16_t *>(in1.ptr()));
+
+                accum.val[0] = vqaddq_qs16(accum.val[0], biases.val[0]);
+                accum.val[1] = vqaddq_qs16(accum.val[1], biases.val[1]);
+
+                vst2q_s16(reinterpret_cast<qint16_t *>(in0_out.ptr()), accum);
+            },
+            in0_out, in1);
+            break;
+        }
         default:
             ARM_COMPUTE_ERROR("Data type not supported");
             break;
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 8c9d12c57c..5bb8b1c22a 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -134,10 +134,14 @@ inline void linearize_volume(const uint8_t *const in_ptr,
     // Append 1 if the convolution layer has biases
     if(has_bias)
     {
-        if(std::is_same<T, arm_compute::qint8_t>::value)
+        if(std::is_same<T, qint8_t>::value)
         {
             *out_ptr = scvt_qs8_f32(1.0f, fixed_point_position);
         }
+        else if(std::is_same<T, qint16_t>::value)
+        {
+            *out_ptr = scvt_qs16_f32(1.0f, fixed_point_position);
+        }
         else
         {
             *out_ptr = static_cast<T>(1);
@@ -249,10 +253,14 @@ void NEIm2ColKernel::run_reduced(const Window &window)
         // Add bias
         if(_has_bias)
         {
-            if(std::is_same<T, arm_compute::qint8_t>::value)
+            if(std::is_same<T, qint8_t>::value)
             {
                 *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = scvt_qs8_f32(1.0f, _input->info()->fixed_point_position());
             }
+            else if(std::is_same<T, qint16_t>::value)
+            {
+                *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = scvt_qs16_f32(1.0f, _input->info()->fixed_point_position());
+            }
             else
             {
                 *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
@@ -269,8 +277,9 @@ NEIm2ColKernel::NEIm2ColKernel()
 
 void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QS8, DataType::QS16);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
 
     _input          = input;
     _output         = output;
@@ -309,6 +318,9 @@ void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size
             case DataType::QS8:
                 _func = &NEIm2ColKernel::run_reduced<qint8_t>;
                 break;
+            case DataType::QS16:
+                _func = &NEIm2ColKernel::run_reduced<qint16_t>;
+                break;
             default:
                 ARM_COMPUTE_ERROR("Data type not supported");
                 break;
@@ -329,6 +341,9 @@ void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size
             case DataType::QS8:
                 _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<qint8_t, false> : &NEIm2ColKernel::run_generic<qint8_t, true>;
                 break;
+            case DataType::QS16:
+                _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<qint16_t, false> : &NEIm2ColKernel::run_generic<qint16_t, true>;
+                break;
             default:
                 ARM_COMPUTE_ERROR("Data type not supported");
                 break;
diff --git a/src/core/NEON/kernels/NETransposeKernel.cpp b/src/core/NEON/kernels/NETransposeKernel.cpp
index a990e9068e..732a0ef4f6 100644
--- a/src/core/NEON/kernels/NETransposeKernel.cpp
+++ b/src/core/NEON/kernels/NETransposeKernel.cpp
@@ -179,7 +179,8 @@ NETransposeKernel::NETransposeKernel()
 
 void NETransposeKernel::configure(const ITensor *input, ITensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::QS16, DataType::U32, DataType::S32, DataType::F16,
+                                                  DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
 
     TensorShape  output_shape{ input->info()->tensor_shape() };
diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
index ac688e1381..d685ec7962 100644
--- a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
+++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
@@ -95,7 +95,7 @@ NEWeightsReshapeKernel::NEWeightsReshapeKernel()
 
 void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
 
     const int          fixed_point_position = input->info()->fixed_point_position();
@@ -129,26 +129,26 @@ void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias
     _bias   = bias;
     _output = output;
 
-    switch(_input->info()->data_type())
+    switch(_input->info()->element_size())
     {
-        case DataType::F32:
+        case 4:
         {
             _func = &weights_reshape<uint32_t>;
             break;
         }
-        case DataType::F16:
+        case 2:
         {
             _func = &weights_reshape<uint16_t>;
             break;
         }
-        case DataType::QS8:
+        case 1:
         {
             _func = &weights_reshape<uint8_t>;
             break;
         }
         default:
         {
-            ARM_COMPUTE_ERROR_ON("Data type not supported");
+            ARM_COMPUTE_ERROR_ON("Element size not supported");
             break;
         }
     }
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index dc8652747f..f6481f1918 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -41,14 +41,13 @@ NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights()
 
 void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
 
     if(biases != nullptr)
     {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::QS8, DataType::F16, DataType::F32);
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
         ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
@@ -96,7 +95,7 @@ NEConvolutionLayer::NEConvolutionLayer()
 
 void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, output);
     ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index 6e27ed344a..eb84ccaddc 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -39,7 +39,7 @@ NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights()
 
 void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
     ARM_COMPUTE_ERROR_ON(output == nullptr);
     ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2);
     ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false));
@@ -196,10 +196,9 @@ void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor *input, const ITens
 
 void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2);
 
     const DataType dt                   = input->info()->data_type();
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 128fb8e842..1cf630a473 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -46,7 +46,7 @@ const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference
 #ifdef ARM_COMPUTE_ENABLE_FP16
 const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
 #endif                             /* ARM_COMPUTE_ENABLE_FP16 */
-const float tolerance_qs8 = 3.0f;  /**< Tolerance value for comparing reference's output against implementation's output for DataType::QS8 */
+const float tolerance_q = 1.0f;    /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
 
 Tensor compute_convolution_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
                                  const PadStrideInfo &conv_info, int fixed_point_position)
@@ -101,7 +101,7 @@ BOOST_AUTO_TEST_SUITE(GEMM)
 
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(Configuration,
-                     AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8 }),
+                     AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }),
                      conv_set, dt)
 {
     // Set fixed point position data type allowed
@@ -188,7 +188,7 @@ BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE(Quantized)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
-                     SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
+                     SmallConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
                      conv_set, dt, fixed_point_position)
 {
     // Compute function
@@ -198,12 +198,12 @@ BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);
 
     // Validate output
-    validate(NEAccessor(dst), ref_dst, tolerance_qs8);
+    validate(NEAccessor(dst), ref_dst, tolerance_q);
 }
 
 BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
-                     AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
+                     AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
                      conv_set, dt, fixed_point_position)
 {
     // Compute function
@@ -213,7 +213,7 @@ BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);
 
     // Validate output
-    validate(NEAccessor(dst), ref_dst, tolerance_qs8);
+    validate(NEAccessor(dst), ref_dst, tolerance_q);
 }
 BOOST_AUTO_TEST_SUITE_END()
 
diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp
index ae0d94a53c..87e0071007 100644
--- a/tests/validation/NEON/FullyConnectedLayer.cpp
+++ b/tests/validation/NEON/FullyConnectedLayer.cpp
@@ -44,7 +44,7 @@ using namespace arm_compute::test::validation;
 namespace
 {
 const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
-const float tolerance_qs8 = 1.0f;   /**< Tolerance value for comparing reference's output against implementation's output for DataType::QS8 */
+const float tolerance_q   = 1.0f;   /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
 
 Tensor compute_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
                                      bool transpose_weights, int fixed_point_position)
@@ -109,7 +109,7 @@ BOOST_AUTO_TEST_SUITE(FullyConnectedLayer)
 
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(Configuration,
-                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8 }),
+                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }),
                      fc_set, dt)
 {
     // Set fixed point position data type allowed
@@ -188,7 +188,7 @@ BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE(Quantized)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(RunSmall,
-                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8 }) * boost::unit_test::data::xrange(4, 7),
+                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
                      fc_set, dt, fixed_point_position)
 {
     // Compute function
@@ -198,12 +198,12 @@ BOOST_DATA_TEST_CASE(RunSmall,
     RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
 
     // Validate output
-    validate(NEAccessor(dst), ref_dst, tolerance_qs8);
+    validate(NEAccessor(dst), ref_dst, tolerance_q);
 }
 
 BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(RunLarge,
-                     LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8 }) * boost::unit_test::data::xrange(4, 7),
+                     LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
                      fc_set, dt, fixed_point_position)
 {
     // Compute function
@@ -213,7 +213,7 @@ BOOST_DATA_TEST_CASE(RunLarge,
     RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
 
     // Validate output
-    validate(NEAccessor(dst), ref_dst, tolerance_qs8);
+    validate(NEAccessor(dst), ref_dst, tolerance_q);
 }
 BOOST_AUTO_TEST_SUITE_END()
 
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h
index 488ffa90d9..0502f53186 100644
--- a/tests/validation/TensorOperations.h
+++ b/tests/validation/TensorOperations.h
@@ -158,7 +158,7 @@ void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi,
     *out = res.raw();
 }
 
-template <typename T>
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
 void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position)
 {
     for(int x = 0; x < cols_weights; ++x)
@@ -172,11 +172,12 @@ void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out
     }
 }
 
-template <>
-void vector_matrix_multiply(const int8_t *in, const int8_t *weights, const int8_t *bias, int8_t *out, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+// Vector matrix multiply for fixed point type
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr>
+void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position)
 {
     using namespace fixed_point_arithmetic;
-    using promoted_type = typename fixed_point_arithmetic::traits::promote<int8_t>::type;
+    using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type;
 
     for(int x = 0; x < cols_weights; ++x)
     {
@@ -192,10 +193,10 @@ void vector_matrix_multiply(const int8_t *in, const int8_t *weights, const int8_
         }
 
         // Get the bias
-        const fixed_point<int8_t> b(bias[x], fixed_point_position, true);
+        const fixed_point<T> b(bias[x], fixed_point_position, true);
 
         // Convert back and accumulate the bias
-        fixed_point<int8_t> res(acc);
+        fixed_point<T> res(acc);
         res = res + b;
 
         // Store the result