COMPMID-594: Implement reference and CL/NEON validation for LocallyConnected

Change-Id: I01e7abcf3f1b19458128e277044af850ad9fa224
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118610
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

4 files changed, 384 insertions, 184 deletions

diff --git a/tests/validation/reference/Convolution3d.h b/tests/validation/reference/Convolution3d.h
new file mode 100644
index 0000000000..b99d534635
--- /dev/null
+++ b/tests/validation/reference/Convolution3d.h
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2017-2018 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:
+ *asymm_int_mult
+ * 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, asymm_int_multDAMAGES 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.
+ */
+#ifndef __ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__
+#define __ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__
+
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/UtilsQuantizedAsymm.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace convolution_3d
+{
+namespace detail
+{
+inline bool is_valid_pixel(int i, int min, int max)
+{
+    return (i >= min && i < max);
+}
+
+// 3D convolution for floating point type
+template < typename T, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TB>::value, int >::type = 0 >
+inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
+                          int i_offset, int w_offset, int b_offset, int o_offset,
+                          int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
+{
+    const T *in_ptr  = in.data() + i_offset;
+    const T *w_ptr   = weights.data() + w_offset;
+    const TB *b_ptr   = bias.data() + b_offset;
+    T        *out_ptr = out.data() + o_offset;
+
+    const int half_width_weights_start  = width_weights / 2;
+    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+    const int half_height_weights_start = height_weights / 2;
+    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+    // Reset accumulator
+    T acc(0);
+
+    // Compute a 2D convolution for each IFM and accumulate the result
+    for(int ifm = 0; ifm < depth_in; ++ifm)
+    {
+        // Compute the offset for the input slice
+        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
+
+        // Compute 2D convolution
+        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
+        {
+            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
+            {
+                // Check if the pixel is out-of-bound
+                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
+                {
+                    const int idx = xk + half_width_weights_start;
+                    const int idy = yk + half_height_weights_start;
+
+                    const T i_value = in_ptr[offset_slice_in + xk + yk * width_in];
+                    const T w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
+
+                    acc += i_value * w_value;
+                }
+            }
+        }
+    }
+
+    // Accumulate the bias and store the result
+    *out_ptr = acc + (*b_ptr);
+}
+
+// 3D convolution for fixed point type
+template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 >
+inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
+                          int i_offset, int w_offset, int b_offset, int o_offset,
+                          int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
+{
+    const T *in_ptr               = in.data() + i_offset;
+    const T *w_ptr                = weights.data() + w_offset;
+    const T *b_ptr                = bias.data() + b_offset;
+    T       *out_ptr              = out.data() + o_offset;
+    int      fixed_point_position = in.fixed_point_position();
+
+    const int half_width_weights_start  = width_weights / 2;
+    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+    const int half_height_weights_start = height_weights / 2;
+    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+    using namespace fixed_point_arithmetic;
+    using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
+
+    // Reset accumulator
+    fixed_point<promoted_type> acc(0, fixed_point_position);
+
+    // Compute a 2D convolution for each IFM and accumulate the result
+    for(int ifm = 0; ifm < depth_in; ++ifm)
+    {
+        // Compute the offset for the input slice
+        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
+
+        // Compute 2D convolution
+        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
+        {
+            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
+            {
+                // Check if the pixel is out-of-bound
+                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
+                {
+                    const int idx = xk + half_width_weights_start;
+                    const int idy = yk + half_height_weights_start;
+
+                    const fixed_point<promoted_type> i_value(in_ptr[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
+                    const fixed_point<promoted_type> w_value(w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
+                    const fixed_point<promoted_type> iw = i_value * w_value;
+                    acc                                 = iw + acc;
+                }
+            }
+        }
+    }
+
+    // Get the bias
+    const fixed_point<promoted_type> b(*b_ptr, fixed_point_position, true);
+
+    // Accumulate the bias and covert back
+    acc = acc + b;
+    fixed_point<T> res(acc);
+    *out_ptr = res.raw();
+}
+
+// 3D convolution for QASYMM8 type
+template <>
+inline void convolution3d(const SimpleTensor<uint8_t> &in, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &out,
+                          int i_offset, int w_offset, int b_offset, int o_offset,
+                          int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
+{
+    const uint8_t *in_ptr  = in.data() + i_offset;
+    const uint8_t *w_ptr   = weights.data() + w_offset;
+    const int32_t *b_ptr   = bias.data() + b_offset;
+    uint8_t       *out_ptr = out.data() + o_offset;
+
+    const int   input_offset   = -in.quantization_info().offset;
+    const float input_scale    = in.quantization_info().scale;
+    const int   weights_offset = -weights.quantization_info().offset;
+    const float weights_scale  = weights.quantization_info().scale;
+    const int   output_offset  = out.quantization_info().offset;
+    const float output_scale   = out.quantization_info().scale;
+
+    int         output_multiplier = 0;
+    int         output_shift      = 0;
+    const float multiplier        = input_scale * weights_scale / output_scale;
+    arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+    const int half_width_weights_start  = width_weights / 2;
+    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+    const int half_height_weights_start = height_weights / 2;
+    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+    // Reset accumulator
+    int32_t acc(0);
+
+    // Compute a 2D convolution for each IFM and accumulate the result
+    for(int ifm = 0; ifm < depth_in; ++ifm)
+    {
+        // Compute the offset for the input slice
+        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
+
+        // Compute 2D convolution
+        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
+        {
+            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
+            {
+                // Check if the pixel is out-of-bound
+                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
+                {
+                    const int idx = xk + half_width_weights_start;
+                    const int idy = yk + half_height_weights_start;
+
+                    const uint8_t i_value = in_ptr[offset_slice_in + xk + yk * width_in];
+                    const uint8_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
+
+                    acc += (i_value + input_offset) * (w_value + weights_offset);
+                }
+            }
+        }
+    }
+
+    // Accumulate the bias
+    acc += (*b_ptr);
+
+    acc = validation::asymm_rounding_divide_by_pow2(validation::asymm_int_mult(acc, output_multiplier), output_shift);
+    acc += output_offset;
+    acc = utility::clamp<int32_t>(acc, 0, 255);
+
+    // Store the result
+    *out_ptr = acc;
+}
+} // namespace detail
+} // namespace convolution_3d
+} // namespace test
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__ */
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp
index b7ed2f56c0..24bbf32a30 100644
--- a/tests/validation/reference/ConvolutionLayer.cpp
+++ b/tests/validation/reference/ConvolutionLayer.cpp
@@ -25,6 +25,7 @@
 
 #include "tests/validation/FixedPoint.h"
 #include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Convolution3d.h"
 #include "tests/validation/reference/Utils.h"
 #include "tests/validation/reference/UtilsQuantizedAsymm.h"
 
@@ -42,185 +43,6 @@ namespace reference
 {
 namespace
 {
-inline bool is_valid_pixel(int i, int min, int max)
-{
-    return (i >= min && i < max);
-}
-
-// 3D convolution for floating point type
-template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 >
-void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
-                   int i_offset, int w_offset, int b_offset, int o_offset,
-                   int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
-{
-    const T *in_ptr  = in.data() + i_offset;
-    const T *w_ptr   = weights.data() + w_offset;
-    const TB *b_ptr   = bias.data() + b_offset;
-    T        *out_ptr = out.data() + o_offset;
-
-    const int half_width_weights_start  = width_weights / 2;
-    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
-    const int half_height_weights_start = height_weights / 2;
-    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
-    // Reset accumulator
-    T acc(0);
-
-    // Compute a 2D convolution for each IFM and accumulate the result
-    for(int ifm = 0; ifm < depth_in; ++ifm)
-    {
-        // Compute the offset for the input slice
-        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
-
-        // Compute 2D convolution
-        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
-        {
-            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
-            {
-                // Check if the pixel is out-of-bound
-                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
-                {
-                    const int idx = xk + half_width_weights_start;
-                    const int idy = yk + half_height_weights_start;
-
-                    const T i_value = in_ptr[offset_slice_in + xk + yk * width_in];
-                    const T w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
-
-                    acc += i_value * w_value;
-                }
-            }
-        }
-    }
-
-    // Accumulate the bias and store the result
-    *out_ptr = acc + (*b_ptr);
-}
-
-// 3D convolution for fixed point type
-template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 >
-void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
-                   int i_offset, int w_offset, int b_offset, int o_offset,
-                   int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
-{
-    const T *in_ptr               = in.data() + i_offset;
-    const T *w_ptr                = weights.data() + w_offset;
-    const T *b_ptr                = bias.data() + b_offset;
-    T       *out_ptr              = out.data() + o_offset;
-    int      fixed_point_position = in.fixed_point_position();
-
-    const int half_width_weights_start  = width_weights / 2;
-    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
-    const int half_height_weights_start = height_weights / 2;
-    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
-    using namespace fixed_point_arithmetic;
-    using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
-
-    // Reset accumulator
-    fixed_point<promoted_type> acc(0, fixed_point_position);
-
-    // Compute a 2D convolution for each IFM and accumulate the result
-    for(int ifm = 0; ifm < depth_in; ++ifm)
-    {
-        // Compute the offset for the input slice
-        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
-
-        // Compute 2D convolution
-        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
-        {
-            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
-            {
-                // Check if the pixel is out-of-bound
-                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
-                {
-                    const int idx = xk + half_width_weights_start;
-                    const int idy = yk + half_height_weights_start;
-
-                    const fixed_point<promoted_type> i_value(in_ptr[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
-                    const fixed_point<promoted_type> w_value(w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
-                    const fixed_point<promoted_type> iw = i_value * w_value;
-                    acc                                 = iw + acc;
-                }
-            }
-        }
-    }
-
-    // Get the bias
-    const fixed_point<promoted_type> b(*b_ptr, fixed_point_position, true);
-
-    // Accumulate the bias and covert back
-    acc = acc + b;
-    fixed_point<T> res(acc);
-    *out_ptr = res.raw();
-}
-
-// 3D convolution for QASYMM8 type
-template <>
-void convolution3d(const SimpleTensor<uint8_t> &in, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &out,
-                   int i_offset, int w_offset, int b_offset, int o_offset,
-                   int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
-{
-    const uint8_t *in_ptr  = in.data() + i_offset;
-    const uint8_t *w_ptr   = weights.data() + w_offset;
-    const int32_t *b_ptr   = bias.data() + b_offset;
-    uint8_t       *out_ptr = out.data() + o_offset;
-
-    const int   input_offset   = -in.quantization_info().offset;
-    const float input_scale    = in.quantization_info().scale;
-    const int   weights_offset = -weights.quantization_info().offset;
-    const float weights_scale  = weights.quantization_info().scale;
-    const int   output_offset  = out.quantization_info().offset;
-    const float output_scale   = out.quantization_info().scale;
-
-    int         output_multiplier = 0;
-    int         output_shift      = 0;
-    const float multiplier        = input_scale * weights_scale / output_scale;
-    arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-
-    const int half_width_weights_start  = width_weights / 2;
-    const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
-    const int half_height_weights_start = height_weights / 2;
-    const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
-    // Reset accumulator
-    int32_t acc(0);
-
-    // Compute a 2D convolution for each IFM and accumulate the result
-    for(int ifm = 0; ifm < depth_in; ++ifm)
-    {
-        // Compute the offset for the input slice
-        const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
-
-        // Compute 2D convolution
-        for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
-        {
-            for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
-            {
-                // Check if the pixel is out-of-bound
-                if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
-                {
-                    const int idx = xk + half_width_weights_start;
-                    const int idy = yk + half_height_weights_start;
-
-                    const uint8_t i_value = in_ptr[offset_slice_in + xk + yk * width_in];
-                    const uint8_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
-
-                    acc += (i_value + input_offset) * (w_value + weights_offset);
-                }
-            }
-        }
-    }
-
-    // Accumulate the bias
-    acc += (*b_ptr);
-
-    acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift);
-    acc += output_offset;
-    acc = utility::clamp<int32_t>(acc, 0, 255);
-
-    // Store the result
-    *out_ptr = acc;
-}
 } // namespace
 
 template <typename T, typename TB>
@@ -270,11 +92,11 @@ SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor
                     ARM_COMPUTE_ASSERT(yo < height_out);
 
                     // Compute 3D convolution
-                    convolution3d(src, weights, bias, dst,
-                                  offset_in, ofm * width_weights * height_weights * depth_weights, ofm, offset_out,
-                                  xi, yi,
-                                  width_in, height_in, depth_in,
-                                  width_weights, height_weights);
+                    convolution_3d::detail::convolution3d(src, weights, bias, dst,
+                                                          offset_in, ofm * width_weights * height_weights * depth_weights, ofm, offset_out,
+                                                          xi, yi,
+                                                          width_in, height_in, depth_in,
+                                                          width_weights, height_weights);
                 }
             }
         }
diff --git a/tests/validation/reference/LocallyConnected.cpp b/tests/validation/reference/LocallyConnected.cpp
new file mode 100644
index 0000000000..08e3f02761
--- /dev/null
+++ b/tests/validation/reference/LocallyConnected.cpp
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2017-2018 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 "LocallyConnected.h"
+
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Convolution3d.h"
+#include "tests/validation/reference/Utils.h"
+
+#include "tests/framework/Asserts.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename TB>
+SimpleTensor<T> locally_connected(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info)
+{
+    // Create reference
+    SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
+
+    // Compute reference
+    const int width_in  = src.shape().x();
+    const int height_in = src.shape().y();
+    const int depth_in  = src.shape().z();
+
+    const int width_out  = dst.shape().x();
+    const int height_out = dst.shape().y();
+    const int depth_out  = dst.shape().z();
+
+    const int width_weights  = weights.shape().x();
+    const int height_weights = weights.shape().y();
+    const int depth_weights  = weights.shape().z();
+
+    const int pad_left  = info.pad_left();
+    const int pad_top   = info.pad_top();
+    const int stride_xi = info.stride().first;
+    const int stride_yi = info.stride().second;
+
+    auto output_wh = scaled_dimensions(width_in, height_in, width_weights, height_weights, info);
+
+    const int start_xi    = width_weights / 2 - pad_left;
+    const int start_yi    = height_weights / 2 - pad_top;
+    const int end_xi      = output_wh.first * stride_xi;
+    const int end_yi      = output_wh.second * stride_yi;
+    const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in);
+
+    for(int r = 0; r < num_batches; ++r)
+    {
+        int count = 0;
+        for(int yi = start_yi; yi < start_yi + end_yi; yi += stride_yi)
+        {
+            for(int xi = start_xi; xi < start_xi + end_xi; xi += stride_xi)
+            {
+                for(int ofm = 0; ofm < depth_out; ++ofm)
+                {
+                    // Compute input and output offsets
+                    const int offset_in  = r * width_in * height_in * depth_in;
+                    const int xo         = (xi - start_xi) / stride_xi;
+                    const int yo         = (yi - start_yi) / stride_yi;
+                    const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out;
+
+                    ARM_COMPUTE_ASSERT(xo < width_out);
+                    ARM_COMPUTE_ASSERT(yo < height_out);
+
+                    // Compute 3D convolution
+                    convolution_3d::detail::convolution3d(src, weights, bias, dst,
+                                                          offset_in, count * width_weights * height_weights * depth_weights, count, offset_out,
+                                                          xi, yi,
+                                                          width_in, height_in, depth_in,
+                                                          width_weights, height_weights);
+                    count++;
+                }
+            }
+        }
+    }
+
+    return dst;
+}
+
+// Locally Connected only supports F32
+template SimpleTensor<float> locally_connected(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
+                                               const PadStrideInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/reference/LocallyConnected.h b/tests/validation/reference/LocallyConnected.h
new file mode 100644
index 0000000000..bf78d2c02a
--- /dev/null
+++ b/tests/validation/reference/LocallyConnected.h
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2017-2018 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.
+ */
+#ifndef __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__
+#define __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__
+
+#include "tests/SimpleTensor.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename TB>
+SimpleTensor<T> locally_connected(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__ */