diff --git a/arm_compute/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.h b/arm_compute/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.h
index 948f47a..fee5055 100644
--- a/arm_compute/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.h
+++ b/arm_compute/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -55,6 +55,15 @@
      * @param[out] output Output tensor to store the result. Data type supported: same as @p input0
      */
     void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLLocallyConnectedMatrixMultiplyKernel
+     *
+     * @param[in] input0 First input tensor info. Data types supported: F32
+     * @param[in] input1 Second input tensor info. Data type supported: same as @p input0
+     * @param[in] output Output tensor info. Data type supported: same as @p input0
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h b/arm_compute/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h
index 5e3410f..19a92c0 100644
--- a/arm_compute/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h
+++ b/arm_compute/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h
@@ -55,6 +55,15 @@
      * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
      */
     void configure(const ITensor *input0, const ITensor *input1, ITensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref NELocallyConnectedMatrixMultiplyKernel
+     *
+     * @param[in] input0 First input tensor info. Data types supported: F16, F32
+     * @param[in] input1 Second input tensor info. Data type supported: same as @p input0
+     * @param[in] output Output tensor info. Data type supported: same as @p input0
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
diff --git a/arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h b/arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h
index f56039f..e5aafd8 100644
--- a/arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h
+++ b/arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -65,6 +65,20 @@
      * @param[in]  conv_info Contains padding and stride information described in @ref PadStrideInfo.
      */
     void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLLocallyConnectedLayer
+     *
+     * @param[in] input     Input tensor info. 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: F32.
+     * @param[in] weights   Weights tensor info. Weights are 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches]. Data type supported:Same as @p input.
+     * @param[in] biases    Biases tensor info. Shared biases supported. Biases are 2D tensor with dimensions [OFM, num_patches]. Data type supported:Same as @p input.
+     * @param[in] output    Output tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     *                      Data types supported: Same as @p input.
+     * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info);
 
     // Inherited methods overridden:
     void run() override;
diff --git a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
index c148c3f..1d5dd3f 100644
--- a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
@@ -91,6 +91,20 @@
      */
     void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
                    unsigned int inner_border_right, unsigned int inner_border_top);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEDeconvolutionLayer
+     *
+     * @param[in] input              Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
+     * @param[in] weights            The 4d weights info with dimensions [width, height, OFM, IFM]. Data type supported: Same as @p input.
+     * @param[in] bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
+     * @param[in] output             Output tensor info. The output has the same number of dimensions as the @p input.
+     * @param[in] info               Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     * @param[in] inner_border_right The number of zeros added to right edge of the input.
+     * @param[in] inner_border_top   The number of zeros added to top edge of the input.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info,
+                           unsigned int inner_border_right, unsigned int inner_border_top);
 
     // Inherited methods overridden:
     void run() override;
diff --git a/arm_compute/runtime/NEON/functions/NELocallyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NELocallyConnectedLayer.h
index 18d2a1d..e07f09c 100644
--- a/arm_compute/runtime/NEON/functions/NELocallyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NELocallyConnectedLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -65,6 +65,20 @@
      * @param[in]  conv_info Contains padding and stride information described in @ref PadStrideInfo.
      */
     void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info);
+    /** Static function to check if given info will lead to a valid configuration of @ref NELocallyConnectedLayer
+     *
+     * @param[in] input     Input tensor info. 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: F16, F32.
+     * @param[in] weights   Weights tensor info. Weights are 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches]. Data type supported:Same as @p input.
+     * @param[in] biases    Biases tensor info. Shared biases supported. Biases are 2D tensor with dimensions [OFM, num_patches]. Data type supported:Same as @p input.
+     * @param[in] output    Output tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     *                      Data types supported: Same as @p input.
+     * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info);
 
     // Inherited methods overridden:
     void run() override;
diff --git a/src/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.cpp
index a3af5b0..84f2e0c 100644
--- a/src/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLLocallyConnectedMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,13 +46,44 @@
 {
 }
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+
+    return Status{};
+}
+
+std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
+{
+    const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->data_type());
+
+    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x));
+
+    AccessWindowHorizontal input0_access(input0, 0, num_elems_processed_per_iteration_x);
+    AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_x);
+    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x);
+
+    bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+
+    return std::make_tuple(err, win);
+}
+} // namespace
+
 void CLLocallyConnectedMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
-    ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
 
     _input0 = input0;
     _input1 = input1;
@@ -77,20 +108,20 @@
     std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type()));
     _kernel                    = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(("gemm_lc_vm_" + data_type_name), build_opts));
 
-    // Configure window kernel
-    const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type());
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info());
 
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+    ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
 
-    AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_x, 1);
-    AccessWindowRectangle input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1);
-    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, 1);
+    ICLKernel::configure(std::get<1>(win_config));
+}
 
-    update_window_and_padding(win, input0_access, input1_access, output_access);
+Status CLLocallyConnectedMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get())));
 
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLLocallyConnectedMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp
index 35beb82..099626d 100644
--- a/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp
@@ -302,6 +302,37 @@
     },
     ina, out);
 }
+
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+
+    return Status{};
+}
+
+std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
+{
+    const unsigned int num_elems_processed_per_iteration_x = 16;
+
+    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x));
+
+    AccessWindowHorizontal input0_access(input0, 0, num_elems_processed_per_iteration_x);
+    AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_x);
+    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x);
+
+    bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+
+    return std::make_tuple(err, win);
+}
 } // namespace
 
 NELocallyConnectedMatrixMultiplyKernel::NELocallyConnectedMatrixMultiplyKernel()
@@ -311,31 +342,27 @@
 
 void NELocallyConnectedMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
 
     _input0 = input0;
     _input1 = input1;
     _output = output;
 
-    const unsigned int num_elems_processed_per_iteration_x = 16;
-
     // Configure kernel window
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info());
 
-    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
+    ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
 
-    update_window_and_padding(win,
-                              AccessWindowHorizontal(input0->info(), 0, num_elems_processed_per_iteration_x),
-                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
-                              output_access);
+    INEKernel::configure(std::get<1>(win_config));
+}
 
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+Status NELocallyConnectedMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get())));
 
-    INEKernel::configure(win);
+    return Status{};
 }
 
 void NELocallyConnectedMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
diff --git a/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp b/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
index d284949..a3eb501 100644
--- a/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
@@ -33,40 +33,102 @@
 
 using namespace arm_compute;
 
+namespace
+{
+void calculate_shapes(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                      TensorShape &shape_wr, TensorShape &shape_im2col, TensorShape &shape_gemm)
+{
+    ARM_COMPUTE_UNUSED(output);
+
+    const unsigned int kernel_width  = weights->dimension(0);
+    const unsigned int kernel_height = weights->dimension(1);
+
+    bool has_bias = (biases != nullptr);
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    const size_t mat_weights_cols = weights->dimension(3);
+    const size_t mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + ((has_bias) ? 1 : 0);
+    const size_t mat_weights_num  = weights->dimension(4);
+
+    shape_wr = TensorShape(mat_weights_cols, mat_weights_rows, mat_weights_num);
+
+    const size_t mat_input_cols = mat_weights_rows;
+    const size_t mat_input_rows = conv_w * conv_h;
+
+    shape_im2col = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+
+    shape_gemm = shape_im2col;
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+}
+} // namespace
+
 CLLocallyConnectedLayer::CLLocallyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),
       _is_first_run(false)
 {
 }
 
+Status CLLocallyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(!conv_info.padding_is_symmetric());
+
+    bool has_bias = (biases != nullptr);
+
+    if(has_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 2);
+    }
+
+    const unsigned int kernel_width  = weights->dimension(0);
+    const unsigned int kernel_height = weights->dimension(1);
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
+
+    // Calculate intermediate buffer shapes
+    TensorShape shape_wr;
+    TensorShape shape_im2col;
+    TensorShape shape_gemm;
+    calculate_shapes(input, weights, biases, output, conv_info, shape_wr, shape_im2col, shape_gemm);
+
+    TensorInfo weights_reshaped_info(shape_wr, 1, weights->data_type());
+    TensorInfo input_im2col_reshaped_info(shape_im2col, 1, input->data_type());
+    TensorInfo gemm_output_info(shape_gemm, 1, input->data_type());
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &input_im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, has_bias));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLWeightsReshapeKernel::validate(weights, biases, &weights_reshaped_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLLocallyConnectedMatrixMultiplyKernel::validate(&input_im2col_reshaped_info, &weights_reshaped_info, &gemm_output_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(&gemm_output_info, output, std::make_pair(conv_w, conv_h)));
+
+    return Status{};
+}
+
 void CLLocallyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
-    ARM_COMPUTE_ERROR_ON(!conv_info.padding_is_symmetric());
-
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 2);
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(CLLocallyConnectedLayer::validate(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info));
 
     bool _has_bias = (biases != nullptr);
     _is_first_run  = true;
 
-    // Get parameters for conv_info
-    unsigned int stride_x = 0;
-    unsigned int stride_y = 0;
-    unsigned int pad_x    = 0;
-    unsigned int pad_y    = 0;
-    std::tie(stride_x, stride_y) = conv_info.stride();
-    std::tie(pad_x, pad_y)       = conv_info.pad();
-
     const unsigned int kernel_width  = weights->info()->dimension(0);
     const unsigned int kernel_height = weights->info()->dimension(1);
 
@@ -76,32 +138,14 @@
     std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
                                                  conv_info);
 
-    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
-    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
-
-    // Create tensor to store the reshaped weights
-    const size_t mat_weights_cols = weights->info()->dimension(3);
-    const size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
-    const size_t mat_weights_num  = weights->info()->dimension(4);
-
-    const TensorShape shape_wr(mat_weights_cols, mat_weights_rows, mat_weights_num);
+    // Calculate intermediate buffer shapes
+    TensorShape shape_wr;
+    TensorShape shape_im2col;
+    TensorShape shape_gemm;
+    calculate_shapes(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info, shape_wr, shape_im2col, shape_gemm);
 
     _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
-
-    // Create tensor to store im2col reshaped inputs
-    const size_t mat_input_cols = mat_weights_rows;
-    const size_t mat_input_rows = conv_w * conv_h;
-    TensorShape  shape_im2col   = input->info()->tensor_shape();
-    shape_im2col.set(0, mat_input_cols);
-    shape_im2col.set(1, mat_input_rows);
-    shape_im2col.set(2, 1);
-
     _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
-
-    // Create locally connected layer output tensor
-    TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
-    shape_gemm.set(0, mat_weights_cols);
-    shape_gemm.set(1, mat_input_rows);
     _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
 
     // Manage intermediate buffers
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
index 693d7a4..14d54d2 100644
--- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
@@ -42,13 +42,64 @@
 {
 }
 
+Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info,
+                                      unsigned int inner_border_right, unsigned int inner_border_top)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
+    ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
+
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
+
+    auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->dimension(1),
+                                                    info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, bias);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, bias);
+
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
+    }
+
+    if(output->tensor_shape().total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+        const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+    }
+
+    TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
+                                                                                                      info)));
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(i) != scale_out_info.dimension(i));
+    }
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayer::validate(&scale_out_info, weights, bias, output, info, WeightsInfo()));
+
+    return Status{};
+}
+
 void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
                                      unsigned int inner_border_right, unsigned int inner_border_top)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
-    ARM_COMPUTE_ERROR_ON(!info.padding_is_symmetric());
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
 
     _input        = input;
     _info         = info;
@@ -56,15 +107,9 @@
 
     const unsigned int stride_x = info.stride().first;
     const unsigned int stride_y = info.stride().second;
-    auto               out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
-                                                                  info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
 
-    const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
-
-    ARM_COMPUTE_UNUSED(output_shape);
-    ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
-    ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
-    ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(NEDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
 
     _memory_group.manage(&_scaled_output);
 
diff --git a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
index 45ddb70..9735594 100644
--- a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
@@ -33,40 +33,102 @@
 
 using namespace arm_compute;
 
+namespace
+{
+void calculate_shapes(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                      TensorShape &shape_wr, TensorShape &shape_im2col, TensorShape &shape_gemm)
+{
+    ARM_COMPUTE_UNUSED(output);
+
+    const unsigned int kernel_width  = weights->dimension(0);
+    const unsigned int kernel_height = weights->dimension(1);
+
+    bool has_bias = (biases != nullptr);
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    const size_t mat_weights_cols = weights->dimension(3);
+    const size_t mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + ((has_bias) ? 1 : 0);
+    const size_t mat_weights_num  = weights->dimension(4);
+
+    shape_wr = TensorShape(mat_weights_cols, mat_weights_rows, mat_weights_num);
+
+    const size_t mat_input_cols = mat_weights_rows;
+    const size_t mat_input_rows = conv_w * conv_h;
+
+    shape_im2col = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+
+    shape_gemm = shape_im2col;
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+}
+} // namespace
+
 NELocallyConnectedLayer::NELocallyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),
       _is_first_run(false)
 {
 }
 
+Status NELocallyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(!conv_info.padding_is_symmetric());
+
+    bool has_bias = (biases != nullptr);
+
+    if(has_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 2);
+    }
+
+    const unsigned int kernel_width  = weights->dimension(0);
+    const unsigned int kernel_height = weights->dimension(1);
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
+
+    // Calculate intermediate buffer shapes
+    TensorShape shape_wr;
+    TensorShape shape_im2col;
+    TensorShape shape_gemm;
+    calculate_shapes(input, weights, biases, output, conv_info, shape_wr, shape_im2col, shape_gemm);
+
+    TensorInfo weights_reshaped_info(shape_wr, 1, weights->data_type());
+    TensorInfo input_im2col_reshaped_info(shape_im2col, 1, input->data_type());
+    TensorInfo gemm_output_info(shape_gemm, 1, input->data_type());
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &input_im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, has_bias, false));
+    ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(NELocallyConnectedMatrixMultiplyKernel::validate(&input_im2col_reshaped_info, &weights_reshaped_info, &gemm_output_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+
+    return Status{};
+}
+
 void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
-    ARM_COMPUTE_ERROR_ON(!conv_info.padding_is_symmetric());
-
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 2);
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NELocallyConnectedLayer::validate(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info));
 
     bool _has_bias = (biases != nullptr);
     _is_first_run  = true;
 
-    // Get parameters for conv_info
-    unsigned int stride_x = 0;
-    unsigned int stride_y = 0;
-    unsigned int pad_x    = 0;
-    unsigned int pad_y    = 0;
-    std::tie(stride_x, stride_y) = conv_info.stride();
-    std::tie(pad_x, pad_y)       = conv_info.pad();
-
     const unsigned int kernel_width  = weights->info()->dimension(0);
     const unsigned int kernel_height = weights->info()->dimension(1);
 
@@ -76,32 +138,14 @@
     std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
                                                  conv_info);
 
-    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
-    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
-
-    // Create tensor to store the reshaped weights
-    const size_t mat_weights_cols = weights->info()->dimension(3);
-    const size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
-    const size_t mat_weights_num  = weights->info()->dimension(4);
-
-    const TensorShape shape_wr(mat_weights_cols, mat_weights_rows, mat_weights_num);
+    // Calculate intermediate buffer shapes
+    TensorShape shape_wr;
+    TensorShape shape_im2col;
+    TensorShape shape_gemm;
+    calculate_shapes(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info, shape_wr, shape_im2col, shape_gemm);
 
     _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
-
-    // Create tensor to store im2col reshaped inputs
-    const size_t mat_input_cols = mat_weights_rows;
-    const size_t mat_input_rows = conv_w * conv_h;
-    TensorShape  shape_im2col   = input->info()->tensor_shape();
-    shape_im2col.set(0, mat_input_cols);
-    shape_im2col.set(1, mat_input_rows);
-    shape_im2col.set(2, 1);
-
     _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
-
-    // Create locally connected layer output tensor
-    TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
-    shape_gemm.set(0, mat_weights_cols);
-    shape_gemm.set(1, mat_input_rows);
     _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
 
     // Manage intermediate buffers
diff --git a/tests/validation/CL/LocallyConnected.cpp b/tests/validation/CL/LocallyConnected.cpp
index 05cab29..de79d60 100644
--- a/tests/validation/CL/LocallyConnected.cpp
+++ b/tests/validation/CL/LocallyConnected.cpp
@@ -47,6 +47,67 @@
 TEST_SUITE(CL)
 TEST_SUITE(LocallyConnected)
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+    framework::dataset::make("InputInfo",  { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Asymmetric padding
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0)
+                                           }),
+    framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("BiasInfo",   { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("PadStride",  { PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 1, 0, 0, 0, DimensionRoundingType::FLOOR),
+                                             PadStrideInfo(2, 1, 0, 0)
+                                           })),
+    framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
+    input_info, weights_info, bias_info, output_info, conv_info, expected)
+{
+    bool is_valid = bool(CLLocallyConnectedLayer::validate(&input_info.clone()->set_is_resizable(false),
+                                                           &weights_info.clone()->set_is_resizable(false),
+                                                           &bias_info.clone()->set_is_resizable(false),
+                                                           &output_info.clone()->set_is_resizable(false),
+                                                           conv_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallLocallyConnectedDataset(), datasets::LargeLocallyConnectedDataset()),
                                                                    framework::dataset::make("DataType", DataType::F32)),
                src_shape, weights_shape, bias_shape, dst_shape, info, data_type)
diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp
index 566b75a..3bb6d6f 100644
--- a/tests/validation/NEON/DeconvolutionLayer.cpp
+++ b/tests/validation/NEON/DeconvolutionLayer.cpp
@@ -58,6 +58,106 @@
 TEST_SUITE(NEON)
 TEST_SUITE(DeconvolutionLayer)
 
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, (combine(datasets::SmallDeconvolutionShapes(), framework::dataset::make("DataType", DataType::F32))),
+               input_shape, data_type)
+{
+    // Create shapes
+    const unsigned int kernel_size_x = 3;
+    const unsigned int kernel_size_y = 3;
+    const unsigned int num_kernels   = 1;
+    const TensorShape  weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
+    const TensorShape  bias_shape(num_kernels);
+    auto               out_dim      = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, 1, 1, 0, 0, 1, 1);
+    TensorShape        output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
+
+    // Create tensors
+    Tensor src     = create_tensor<Tensor>(input_shape, data_type, 1);
+    Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1);
+    Tensor bias    = create_tensor<Tensor>(bias_shape, data_type, 1);
+    Tensor dst     = create_tensor<Tensor>(output_shape, data_type, 1);
+
+    ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    NEDeconvolutionLayer deconv;
+    deconv.configure(&src, &weights, &bias, &dst, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), 0, 0);
+
+    // Validate valid region
+    const ValidRegion src_valid_region     = shape_to_valid_region(input_shape);
+    const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape);
+    const ValidRegion bias_valid_region    = shape_to_valid_region(bias_shape);
+    const ValidRegion dst_valid_region     = shape_to_valid_region(output_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);
+}
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),   // Mismatching data type
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),   // Invalid weights shape
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 4),   // Non supported data type
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 11),  // Invalid bias shape
+                                            TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32, 0), // Window shrink
+                                            TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+                                          }),
+    framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::QS8, 5),
+                                            TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32, 11),
+                                            TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32, 0),
+                                              TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("BiasInfo",  { TensorInfo(TensorShape(1U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 5),
+                                            TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, 11),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 5),
+                                            TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 1, 1),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                           })),
+    framework::dataset::make("ax",          {   1U,
+                                                1U,
+                                                1U,
+                                                1U,
+                                                0U,
+                                                0U,
+                                            })),
+   framework::dataset::make("ay",           {   1U,
+                                                1U,
+                                                1U,
+                                                1U,
+                                                0U,
+                                                0U,
+                                            })),
+    framework::dataset::make("Expected", { false, false, false, false, false, true })),
+    input_info, weights_info, bias_info, output_info, pad_info, ax, ay, expected)
+{
+    bool is_valid = bool(NEDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_info, ax, ay));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NEDeconvolutionLayerFixture4x4 = DeconvolutionValidationFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 4, 4>;
 
diff --git a/tests/validation/NEON/LocallyConnected.cpp b/tests/validation/NEON/LocallyConnected.cpp
index 56430d9..b00f274 100644
--- a/tests/validation/NEON/LocallyConnected.cpp
+++ b/tests/validation/NEON/LocallyConnected.cpp
@@ -48,6 +48,67 @@
 TEST_SUITE(NEON)
 TEST_SUITE(LocallyConnected)
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+    framework::dataset::make("InputInfo",  { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Asymmetric padding
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0)
+                                           }),
+    framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("BiasInfo",   { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("PadStride",  { PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 1, 0, 0, 0, DimensionRoundingType::FLOOR),
+                                             PadStrideInfo(2, 1, 0, 0)
+                                           })),
+    framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
+    input_info, weights_info, bias_info, output_info, conv_info, expected)
+{
+    bool is_valid = bool(NELocallyConnectedLayer::validate(&input_info.clone()->set_is_resizable(false),
+                                                           &weights_info.clone()->set_is_resizable(false),
+                                                           &bias_info.clone()->set_is_resizable(false),
+                                                           &output_info.clone()->set_is_resizable(false),
+                                                           conv_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallLocallyConnectedDataset(), datasets::LargeLocallyConnectedDataset()),
                                                                    framework::dataset::make("DataType", DataType::F32)),
                src_shape, weights_shape, bias_shape, dst_shape, info, data_type)