COMPMID-415: Move FullyConnectedLayer to new validation

Change-Id: I7f60d6fb484d3962b88874e1531cec734c11e416
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83556
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

8 files changed, 0 insertions, 626 deletions

diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp
deleted file mode 100644
index 21ea5a5064..0000000000
--- a/tests/validation/CL/FullyConnectedLayer.cpp
+++ /dev/null
@@ -1,221 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "CL/CLAccessor.h"
-#include "TypePrinter.h"
-#include "dataset/FullyConnectedLayerDataset.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-#include "validation/Datasets.h"
-#include "validation/Reference.h"
-#include "validation/Validation.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
-
-#include <random>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-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_q   = 1.0f;   /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
-
-CLTensor 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)
-{
-    // Create tensors
-    CLTensor src  = create_tensor<CLTensor>(input_shape, dt, 1, fixed_point_position);
-    CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position);
-    CLTensor dst  = create_tensor<CLTensor>(output_shape, dt, 1, fixed_point_position);
-
-    // Swap the first and second dimension of weights' shape if transpose_weights is true
-    TensorShape ws = weights_shape;
-    if(transpose_weights)
-    {
-        const size_t dimx = ws.x();
-        ws.set(0, ws.y());
-        ws.set(1, dimx);
-    }
-
-    CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position);
-
-    // Create and configure function.
-    // Note: We pass the weights already transposed
-    CLFullyConnectedLayer fc;
-    fc.configure(&src, &weights, &bias, &dst, false);
-
-    // Allocate tensors
-    src.allocator()->allocate();
-    weights.allocator()->allocate();
-    bias.allocator()->allocate();
-    dst.allocator()->allocate();
-
-    BOOST_TEST(!src.info()->is_resizable());
-    BOOST_TEST(!weights.info()->is_resizable());
-    BOOST_TEST(!bias.info()->is_resizable());
-    BOOST_TEST(!dst.info()->is_resizable());
-
-    // Fill tensors
-    if(dt == DataType::F32)
-    {
-        std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
-        library->fill(CLAccessor(src), distribution, 0);
-        library->fill(CLAccessor(weights), distribution, 1);
-        library->fill(CLAccessor(bias), distribution, 2);
-    }
-    else
-    {
-        library->fill_tensor_uniform(CLAccessor(src), 0);
-        library->fill_tensor_uniform(CLAccessor(weights), 1);
-        library->fill_tensor_uniform(CLAccessor(bias), 2);
-    }
-
-    // Compute NEFullyConnectedLayer function
-    fc.run();
-
-    return dst;
-}
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(CL)
-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, DataType::QS16 }),
-                     fc_set, dt)
-{
-    // Set fixed point position data type allowed
-    int fixed_point_position = (dt == DataType::F32) ? 0 : 3;
-
-    // Create tensors
-    CLTensor src  = create_tensor<CLTensor>(fc_set.src_shape, dt, 1, fixed_point_position);
-    CLTensor bias = create_tensor<CLTensor>(fc_set.bias_shape, dt, 1, fixed_point_position);
-    CLTensor dst  = create_tensor<CLTensor>(fc_set.dst_shape, dt, 1, fixed_point_position);
-
-    // Swap the first and second dimension of weights' shape if transpose_weights is true
-    TensorShape ws = fc_set.weights_shape;
-    if(fc_set.transpose_weights)
-    {
-        const size_t dimx = ws.x();
-        ws.set(0, ws.y());
-        ws.set(1, dimx);
-    }
-
-    CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position);
-
-    BOOST_TEST(src.info()->is_resizable());
-    BOOST_TEST(weights.info()->is_resizable());
-    BOOST_TEST(bias.info()->is_resizable());
-    BOOST_TEST(dst.info()->is_resizable());
-
-    // Create and configure function.
-    // Note: We pass the weights already transposed
-    CLFullyConnectedLayer fc;
-    fc.configure(&src, &weights, &bias, &dst, false);
-
-    // Validate valid region
-    const ValidRegion src_valid_region     = shape_to_valid_region(fc_set.src_shape);
-    const ValidRegion weights_valid_region = shape_to_valid_region(ws);
-    const ValidRegion bias_valid_region    = shape_to_valid_region(fc_set.bias_shape);
-    const ValidRegion dst_valid_region     = shape_to_valid_region(fc_set.dst_shape);
-
-    validate(src.info()->valid_region(), src_valid_region);
-    validate(weights.info()->valid_region(), weights_valid_region);
-    validate(bias.info()->valid_region(), bias_valid_region);
-    validate(dst.info()->valid_region(), dst_valid_region);
-}
-
-BOOST_AUTO_TEST_SUITE(Float)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
-                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
-                     fc_set, dt)
-{
-    // Compute function
-    CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
-    // Compute reference
-    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, 0);
-
-    // Validate output
-    validate(CLAccessor(dst), ref_dst, tolerance_f32);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
-                     LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
-                     fc_set, dt)
-{
-    // Compute function
-    CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
-    // Compute reference
-    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, 0);
-
-    // Validate output
-    validate(CLAccessor(dst), ref_dst, tolerance_f32);
-}
-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, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
-                     fc_set, dt, fixed_point_position)
-{
-    // Compute function
-    CLTensor dst = compute_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);
-
-    // Compute reference
-    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(CLAccessor(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, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
-                     fc_set, dt, fixed_point_position)
-{
-    // Compute function
-    CLTensor dst = compute_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);
-
-    // Compute reference
-    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(CLAccessor(dst), ref_dst, tolerance_q);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif // DOXYGEN_SKIP_THIS
diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp
deleted file mode 100644
index 22572ecbf8..0000000000
--- a/tests/validation/NEON/FullyConnectedLayer.cpp
+++ /dev/null
@@ -1,243 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "NEON/Accessor.h"
-#include "TypePrinter.h"
-#include "dataset/FullyConnectedLayerDataset.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-#include "validation/Datasets.h"
-#include "validation/Reference.h"
-#include "validation/Validation.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
-
-#include <random>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-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_q   = 1.0f;   /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
-#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*/
-
-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)
-{
-    // Create tensors
-    Tensor src  = create_tensor<Tensor>(input_shape, dt, 1, fixed_point_position);
-    Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position);
-    Tensor dst  = create_tensor<Tensor>(output_shape, dt, 1, fixed_point_position);
-
-    // Swap the first and second dimension of weights' shape if transpose_weights is true
-    TensorShape ws = weights_shape;
-    if(transpose_weights)
-    {
-        const size_t dimx = ws.x();
-        ws.set(0, ws.y());
-        ws.set(1, dimx);
-    }
-
-    Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position);
-
-    // Create and configure function.
-    // Note: We pass the weights already transposed
-    NEFullyConnectedLayer fc;
-    fc.configure(&src, &weights, &bias, &dst, false);
-
-    // Allocate tensors
-    src.allocator()->allocate();
-    weights.allocator()->allocate();
-    bias.allocator()->allocate();
-    dst.allocator()->allocate();
-
-    BOOST_TEST(!src.info()->is_resizable());
-    BOOST_TEST(!weights.info()->is_resizable());
-    BOOST_TEST(!bias.info()->is_resizable());
-    BOOST_TEST(!dst.info()->is_resizable());
-
-    // Fill tensors
-    if(dt == DataType::F16 || dt == DataType::F32)
-    {
-        std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
-        library->fill(Accessor(src), distribution, 0);
-        library->fill(Accessor(weights), distribution, 1);
-        library->fill(Accessor(bias), distribution, 2);
-    }
-    else
-    {
-        library->fill_tensor_uniform(Accessor(src), 0);
-        library->fill_tensor_uniform(Accessor(weights), 1);
-        library->fill_tensor_uniform(Accessor(bias), 2);
-    }
-
-    // Compute NEFullyConnectedLayer function
-    fc.run();
-
-    return dst;
-}
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(NEON)
-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, DataType::QS16 }),
-                     fc_set, dt)
-{
-    // Set fixed point position data type allowed
-    int fixed_point_position = (dt == DataType::F32) ? 0 : 3;
-
-    // Create tensors
-    Tensor src  = create_tensor<Tensor>(fc_set.src_shape, dt, 1, fixed_point_position);
-    Tensor bias = create_tensor<Tensor>(fc_set.bias_shape, dt, 1, fixed_point_position);
-    Tensor dst  = create_tensor<Tensor>(fc_set.dst_shape, dt, 1, fixed_point_position);
-
-    // Swap the first and second dimension of weights' shape if transpose_weights is true
-    TensorShape ws = fc_set.weights_shape;
-    if(fc_set.transpose_weights)
-    {
-        const size_t dimx = ws.x();
-        ws.set(0, ws.y());
-        ws.set(1, dimx);
-    }
-
-    Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position);
-
-    BOOST_TEST(src.info()->is_resizable());
-    BOOST_TEST(weights.info()->is_resizable());
-    BOOST_TEST(bias.info()->is_resizable());
-    BOOST_TEST(dst.info()->is_resizable());
-
-    // Create and configure function.
-    // Note: We pass the weights already transposed
-    NEFullyConnectedLayer fc;
-    fc.configure(&src, &weights, &bias, &dst, false);
-
-    // Validate valid region
-    const ValidRegion src_valid_region     = shape_to_valid_region(fc_set.src_shape);
-    const ValidRegion weights_valid_region = shape_to_valid_region(ws);
-    const ValidRegion bias_valid_region    = shape_to_valid_region(fc_set.bias_shape);
-    const ValidRegion dst_valid_region     = shape_to_valid_region(fc_set.dst_shape);
-
-    validate(src.info()->valid_region(), src_valid_region);
-    validate(weights.info()->valid_region(), weights_valid_region);
-    validate(bias.info()->valid_region(), bias_valid_region);
-    validate(dst.info()->valid_region(), dst_valid_region);
-}
-
-#ifdef ARM_COMPUTE_ENABLE_FP16
-BOOST_AUTO_TEST_SUITE(Float16)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
-                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F16 }),
-                     fc_set, dt)
-{
-    // Compute function
-    Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
-    // Compute reference
-    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, 0);
-
-    // Validate output
-    validate(Accessor(dst), ref_dst, tolerance_f16);
-}
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* ARM_COMPUTE_ENABLE_FP16 */
-
-BOOST_AUTO_TEST_SUITE(Float)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
-                     SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
-                     fc_set, dt)
-{
-    // Compute function
-    Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
-    // Compute reference
-    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, 0);
-
-    // Validate output
-    validate(Accessor(dst), ref_dst, tolerance_f32);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
-                     LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
-                     fc_set, dt)
-{
-    // Compute function
-    Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
-    // Compute reference
-    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, 0);
-
-    // Validate output
-    validate(Accessor(dst), ref_dst, tolerance_f32);
-}
-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, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
-                     fc_set, dt, fixed_point_position)
-{
-    // Compute function
-    Tensor dst = compute_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);
-
-    // Compute reference
-    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(Accessor(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, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
-                     fc_set, dt, fixed_point_position)
-{
-    // Compute function
-    Tensor dst = compute_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);
-
-    // Compute reference
-    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(Accessor(dst), ref_dst, tolerance_q);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* DOXYGEN_SKIP_THIS */
diff --git a/tests/validation/Reference.cpp b/tests/validation/Reference.cpp
index 16b4cb9217..1ea017e998 100644
--- a/tests/validation/Reference.cpp
+++ b/tests/validation/Reference.cpp
@@ -461,46 +461,6 @@ RawTensor Reference::compute_reference_batch_normalization_layer(const TensorSha
     return ref_dst;
 }
 
-RawTensor Reference::compute_reference_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)
-{
-    // Create reference
-    RawTensor ref_src(input_shape, dt, 1, fixed_point_position);
-    RawTensor ref_bias(bias_shape, dt, 1, fixed_point_position);
-    RawTensor ref_dst(output_shape, dt, 1, fixed_point_position);
-
-    // Swap the first and second dimension of weights' shape if transpose_weights is true
-    TensorShape ws = weights_shape;
-    if(transpose_weights)
-    {
-        const size_t dimx = ws.x();
-        ws.set(0, ws.y());
-        ws.set(1, dimx);
-    }
-
-    RawTensor ref_weights(ws, dt, 1, fixed_point_position);
-
-    // Fill reference
-    if(dt == DataType::F16 || dt == DataType::F32)
-    {
-        std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
-        library->fill(ref_src, distribution, 0);
-        library->fill(ref_weights, distribution, 1);
-        library->fill(ref_bias, distribution, 2);
-    }
-    else
-    {
-        library->fill_tensor_uniform(ref_src, 0);
-        library->fill_tensor_uniform(ref_weights, 1);
-        library->fill_tensor_uniform(ref_bias, 2);
-    }
-
-    // Compute reference
-    ReferenceCPP::fully_connected_layer(ref_src, ref_weights, ref_bias, ref_dst);
-
-    return ref_dst;
-}
-
 RawTensor Reference::compute_reference_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position)
 {
     // Create reference
diff --git a/tests/validation/Reference.h b/tests/validation/Reference.h
index 93e12fff26..288dc0e3f7 100644
--- a/tests/validation/Reference.h
+++ b/tests/validation/Reference.h
@@ -293,20 +293,6 @@ public:
      * @return Computed raw tensor.
      */
     static RawTensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0);
-    /** Compute reference for fully connected layer function
-     *
-     * @param[in] input_shape          Shape for the input tensor
-     * @param[in] weights_shape        Shape for the weights tensor
-     * @param[in] bias_shape           Shape for the bias tensor
-     * @param[in] output_shape         Shape for the output tensor
-     * @param[in] dt                   Data type to use
-     * @param[in] transpose_weights    Transpose the weights if true
-     * @param[in] fixed_point_position Number of bits for the fractional part of the fixed point numbers
-     *
-     * @return Computed raw tensor.
-     */
-    static RawTensor compute_reference_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);
     /** Compute reference pooling layer.
       *
       * @param[in] shape_in             Shape of the input tensor.
diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp
index 3bf70a0a19..58b47f9d81 100644
--- a/tests/validation/ReferenceCPP.cpp
+++ b/tests/validation/ReferenceCPP.cpp
@@ -281,16 +281,6 @@ void ReferenceCPP::batch_normalization_layer(const RawTensor &src, RawTensor &ds
     boost::apply_visitor(tensor_visitors::batch_normalization_layer_visitor(s, m, v, b, g, epsilon, fixed_point_position), d);
 }
 
-// Fully connected layer
-void ReferenceCPP::fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst)
-{
-    const TensorVariant s = TensorFactory::get_tensor(src);
-    const TensorVariant w = TensorFactory::get_tensor(weights);
-    const TensorVariant b = TensorFactory::get_tensor(bias);
-    TensorVariant       d = TensorFactory::get_tensor(dst);
-    boost::apply_visitor(tensor_visitors::fully_connected_layer_visitor(s, w, b), d);
-}
-
 // Pooling Layer
 void ReferenceCPP::pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info)
 {
diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h
index ce424bed03..29612d1e3b 100644
--- a/tests/validation/ReferenceCPP.h
+++ b/tests/validation/ReferenceCPP.h
@@ -259,14 +259,6 @@ public:
      */
     static void batch_normalization_layer(const RawTensor &src, RawTensor &dst, const RawTensor &mean, const RawTensor &var, const RawTensor &beta, const RawTensor &gamma, float epsilon,
                                           int fixed_point_position = 0);
-    /** Fully connected layer function
-     *
-     * @param[in]  src     Input tensor
-     * @param[in]  weights Weights tensor.
-     * @param[in]  bias    Bias tensor.
-     * @param[out] dst     Result tensor.
-     */
-    static void fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst);
     /** Pooling layer of @p src based on the information from @p pool_info.
      *
      * @param[in]  src       Input tensor.
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h
index c4884be7ca..f5be139dcf 100644
--- a/tests/validation/TensorOperations.h
+++ b/tests/validation/TensorOperations.h
@@ -58,52 +58,6 @@ struct is_floating_point
 {
 };
 
-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)
-    {
-        T acc(0);
-        for(int y = 0; y < rows_weights; ++y)
-        {
-            acc += in[y] * weights[x + y * cols_weights];
-        }
-        out[x] = acc + bias[x];
-    }
-}
-
-// 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<T>::type;
-
-    for(int x = 0; x < cols_weights; ++x)
-    {
-        // Reset accumulator
-        fixed_point<promoted_type> acc(0, fixed_point_position);
-
-        for(int y = 0; y < rows_weights; ++y)
-        {
-            const fixed_point<promoted_type> i_value(in[y], fixed_point_position, true);
-            const fixed_point<promoted_type> w_value(weights[x + y * cols_weights], fixed_point_position, true);
-            const fixed_point<promoted_type> iw = i_value * w_value;
-            acc                                 = iw + acc;
-        }
-
-        // Get the bias
-        const fixed_point<T> b(bias[x], fixed_point_position, true);
-
-        // Convert back and accumulate the bias
-        fixed_point<T> res(acc);
-        res = res + b;
-
-        // Store the result
-        out[x] = res.raw();
-    }
-}
-
 // Return a tensor element at a specified coordinate with different border modes
 template <typename T>
 T tensor_elem_at(const Tensor<T> &in, Coordinates coord, BorderMode border_mode, T constant_border_value)
@@ -1117,28 +1071,6 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor
     }
 }
 
-// Fully connected layer
-template <typename T>
-void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out)
-{
-    ARM_COMPUTE_ERROR_ON(weights.shape().x() != out.shape().x());
-    ARM_COMPUTE_ERROR_ON(weights.shape().y() != in.shape().x() * in.shape().y() * in.shape().z());
-    const int cols_weights = weights.shape().x();
-    const int rows_weights = weights.shape().y();
-    const int num_batches  = in.shape().total_size() / rows_weights;
-
-    for(int k = 0; k < num_batches; ++k)
-    {
-        vector_matrix_multiply<T>(in.data() + k * rows_weights,
-                                  weights.data(),
-                                  bias.data(),
-                                  out.data() + k * cols_weights,
-                                  cols_weights,
-                                  rows_weights,
-                                  in.fixed_point_position());
-    }
-}
-
 // Pooling layer
 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
 void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info)
diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h
index 193697acf0..732cd0e8f1 100644
--- a/tests/validation/TensorVisitors.h
+++ b/tests/validation/TensorVisitors.h
@@ -232,28 +232,6 @@ private:
     float                _epsilon;
     int                  _fixed_point_position;
 };
-// Fully Connected Layer visitor
-struct fully_connected_layer_visitor : public boost::static_visitor<>
-{
-public:
-    explicit fully_connected_layer_visitor(const TensorVariant &in, const TensorVariant &weights, const TensorVariant &bias)
-        : _in(in), _weights(weights), _bias(bias)
-    {
-    }
-    template <typename T>
-    void operator()(Tensor<T> &out) const
-    {
-        const Tensor<T> &in      = boost::get<Tensor<T>>(_in);
-        const Tensor<T> &weights = boost::get<Tensor<T>>(_weights);
-        const Tensor<T> &bias    = boost::get<Tensor<T>>(_bias);
-        tensor_operations::fully_connected_layer(in, weights, bias, out);
-    }
-
-private:
-    const TensorVariant &_in;
-    const TensorVariant &_weights;
-    const TensorVariant &_bias;
-};
 
 // Pooling layer
 struct pooling_layer_visitor : public boost::static_visitor<>