diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 5ca8eb8..8f7d940 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -44,6 +44,16 @@
 
 namespace arm_compute
 {
+namespace
+{
+TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool has_bias)
+{
+    const unsigned int mat_weights_cols = weights->dimension(3);
+    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+    return TensorShape(mat_weights_cols, mat_weights_rows);
+}
+} // namespace
+
 NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
 {
@@ -51,18 +61,12 @@
 
 void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
-
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
+    // Perform validation step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
+                                                                          (biases != nullptr) ? biases->info() : nullptr,
+                                                                          output->info(),
+                                                                          transpose1xW));
 
     // Check if bias are present, if yes they will be embedded to the weights matrix
     const bool _has_bias = (biases != nullptr);
@@ -72,10 +76,7 @@
     if(transpose1xW)
     {
         // Create tensor to store the reshaped weights
-        const unsigned int mat_weights_cols = weights->info()->dimension(3);
-        const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
-        TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
-        TensorInfo         info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
+        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), _has_bias));
 
         _weights_reshaped.allocator()->init(info_wr);
         _memory_group.manage(&_weights_reshaped);
@@ -91,6 +92,46 @@
     }
 }
 
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool has_bias = (biases != nullptr);
+
+    // Checks performed when biases are present
+    if(has_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(transpose1xW)
+    {
+        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, has_bias));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
+    }
+
+    return Status{};
+}
+
 void NEConvolutionLayerReshapeWeights::run()
 {
     _memory_group.acquire();
@@ -105,6 +146,62 @@
     _memory_group.release();
 }
 
+namespace
+{
+TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has_bias, bool is_fully_connected_convolution)
+{
+    unsigned int mat_weights_cols = weights->dimension(3);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+
+    if(is_fully_connected_convolution)
+    {
+        // Create tensor to store the reshaped weights
+        return TensorShape(mat_weights_cols, mat_weights_rows);
+    }
+    else
+    {
+        // Create tensor to store transposed weights
+        const float transpose_width = 16.0f / weights->element_size();
+        return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+    }
+}
+
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
+                                      bool &has_bias,
+                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+                                      unsigned int &conv_w, unsigned int &conv_h)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    dt                   = input->data_type();
+    has_bias             = (biases != nullptr);
+    are_weights_reshaped = weights_info.are_reshaped();
+    kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
+    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+    mat_weights_cols     = weights->dimension(3);
+    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+
+    return Status{};
+}
+} // namespace
+
 NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
       _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
@@ -113,42 +210,25 @@
 
 void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
-    ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
 
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
-        ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
+    DataType     dt{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
 
-    const DataType dt                   = input->info()->data_type();
-    const int      fixed_point_position = input->info()->fixed_point_position();
+    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _has_bias, _are_weights_reshaped,
+                                                   kernel_width, kernel_height,
+                                                   _is_fully_connected_convolution,
+                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
 
-    _has_bias             = (biases != nullptr);
-    _are_weights_reshaped = weights_info.are_reshaped();
+    ARM_COMPUTE_ERROR_THROW_ON(status);
 
-    // Get parameters from conv_info
-    unsigned int stride_x = 0;
-    unsigned int stride_y = 0;
-    std::tie(stride_x, stride_y) = conv_info.stride();
-
-    // Get convolved dimensions
-    unsigned int conv_w = 0;
-    unsigned int conv_h = 0;
-
-    const unsigned int kernel_width  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
-    const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
-    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
-                                                 conv_info);
-
-    // Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels
-    _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    const unsigned int fixed_point_position = input->info()->fixed_point_position();
 
 #if defined(__arm__)
     if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
@@ -162,9 +242,6 @@
     }
 #endif /* defined(__arm__) || defined(__aarch64__) */
 
-    unsigned int mat_weights_cols = weights->info()->dimension(3);
-    unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
-
     // Reshape weights if needed
     if(_mm_optimised_kernel != nullptr)
     {
@@ -230,7 +307,7 @@
     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, dt, fixed_point_position));
+    _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
     _memory_group.manage(&_input_im2col_reshaped);
 
     // Create tensor (interleave) to prepare input tensor for GEMM
@@ -239,7 +316,7 @@
         TensorShape shape_interleaved(shape_im2col);
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-        _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
+        _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
         _memory_group.manage(&_input_interleaved_reshaped);
     }
 
@@ -247,7 +324,7 @@
     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, dt, fixed_point_position));
+    _gemm_output.allocator()->init(_input_im2col_reshaped.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm));
     _memory_group.manage(&_gemm_output);
 
     // Configure kernels
@@ -296,8 +373,6 @@
     _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
     _gemm_output.allocator()->allocate();
 
-    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");
-
     // Allocate intermediate tensor
     if(!_are_weights_reshaped)
     {
@@ -305,6 +380,128 @@
     }
 }
 
+Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                    const WeightsInfo &weights_info)
+{
+    DataType     dt{};
+    bool         has_bias{};
+    bool         are_weights_reshaped{};
+    bool         is_fully_connected_convolution{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, has_bias, are_weights_reshaped, kernel_width, kernel_height,
+                                                   is_fully_connected_convolution, mat_weights_cols, mat_weights_rows,
+                                                   conv_w, conv_h);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(status);
+
+    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
+    bool                         optimised_kernel = false;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(optimised_kernel)
+    {
+        if(are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+    else
+    {
+        if(are_weights_reshaped)
+        {
+            const unsigned int transpose_width = 16 / input->element_size();
+            mat_weights_cols                   = weights_info.num_kernels();
+            mat_weights_rows                   = weights->dimension(0) / transpose_width + (has_bias ? 1 : 0);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(is_fully_connected_convolution)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+
+    // Validate im2col
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+    TensorShape        shape_im2col   = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, has_bias));
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(im2_col_info.tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
+
+    // Validate GEMM interleave and multiply
+    if(!is_fully_connected_convolution)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+    }
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    return Status{};
+}
+
 void NEConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)