diff --git a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h
index f80985a..59cdf33 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h
@@ -50,6 +50,22 @@
      */
     void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
                    ActivationLayerInfo act_info) override;
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NCHWKernel
+     *
+     * @param[in] input            Source tensor. DataType supported: F16/F32/QASYMM8.
+     * @param[in] weights          Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. Data type supported: Same as @p input.
+     * @param[in] biases           Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed.
+     *                             Data type supported: Same as @p input.
+     * @param[in] output           Destination tensor. Data type supported: Same as @p input.
+     * @param[in] conv_info        Padding and stride information to use for the convolution.
+     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported.
+     * @param[in] gpu_target       (Optional) GPU target to validate the kernel for. Defaults to midgard.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+                           ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD);
 
     void run(const Window &window, cl::CommandQueue &queue) override;
     BorderSize border_size() const override;
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h
index 3f3e361..00d9cb6 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h
@@ -60,6 +60,20 @@
      * @param[in]  depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
      */
     void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseIm2ColKernel
+     *
+     * @param[in] input            The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                             while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F32
+     * @param[in] output           The output tensor. First 3 lower dimensions represent a transform of each 3D input,
+     *                             while every dimension above 3 represents a batch. Data types supported: Same as @p input
+     * @param[in] kernel_dims      The kernel dimensions (width and height).
+     * @param[in] conv_info        Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in] has_bias         Boolean that specifies if the depthwise convolution has bias.
+     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseVectorToTensorKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseVectorToTensorKernel.h
index 36d7cad..c9ec8e1 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseVectorToTensorKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseVectorToTensorKernel.h
@@ -58,7 +58,16 @@
      * @param[in]  conv_h The converted tensor's height.
      */
     void configure(const ICLTensor *input, ICLTensor *output, size_t conv_w, size_t conv_h);
-
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseVectorToTensorKernel
+     *
+     * @param[in] input  The input vector to convert. Data type supported: QASYMM8/S32/F16/F32.
+     * @param[in] output The output tensor. 3 lower dimensions represent a single input [width, height, IFM]. Data type supported: same as @p input.
+     * @param[in] conv_w The converted tensor's width.
+     * @param[in] conv_h The converted tensor's height.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h);
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
 
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.h
index 1c1eaca..34ffa17 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.h
@@ -56,6 +56,16 @@
      * @param[in]  biases (Optional) The input biases to add. Shape [IFM]. Data type supported: same as @p input.
      */
     void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *biases = nullptr);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseWeightsReshapeKernel
+     *
+     * @param[in] input  The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM].
+     *                   Data type supported: QASYMM8/F32.
+     * @param[in] output The output tensor. Data type supported: same as @p input.
+     * @param[in] biases (Optional) The input biases to add. Shape [IFM]. Data type supported: same as @p input.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases = nullptr);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.h b/arm_compute/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.h
index c27307e..6390f86 100644
--- a/arm_compute/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.h
@@ -51,6 +51,15 @@
      * @param[out] output The output 2D tensor. Data types supported: Same as @p input
      */
     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 CLGEMMMatrixVectorMultiplyKernel
+     *
+     * @param[in] input0 The reshaped input tensor. Data types supported: QASYMM8/F16/F32
+     * @param[in] input1 The 2D reshaped weights tensor. Data type supported: Same as @p input, S32 for QASYMM8 input.
+     * @param[in] output The output 2D tensor. Data types supported: Same as @p input
+     *
+     * @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/runtime/CL/functions/CLDepthwiseConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h
index 6e5ce4c..b1eb4b9 100644
--- a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h
@@ -67,6 +67,22 @@
     void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1,
                    ActivationLayerInfo act_info = ActivationLayerInfo());
 
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3
+     *
+     * @param[in] input            Source tensor. Data type supported: QASYMM8 for all layouts, F16/F32 for NCHW.
+     * @param[in] weights          Weights tensor. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as @p input.
+     * @param[in] biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
+     *                             Data type supported: Same as @p input, S32 when input is QASYMM8.
+     * @param[in] output           Destination tensor. Data type supported: same as @p input.
+     * @param[in] conv_info        Padding and stride information to use for the convolution.
+     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported.
+     * @param[in] gpu_target       (Optional) GPU target to validate the kernel for. Defaults to midgard.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1,
+                           ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD);
     // Inherited methods overriden:
     void run() override;
 
@@ -108,6 +124,20 @@
      */
     void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1);
 
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer
+     *
+     * @param[in] input            Source tensor. Data type supported: QASYMM8/F32.
+     * @param[in] weights          Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input.
+     * @param[in] biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
+     *                             Data type supported: Same as @p input, S32 when input is QASYMM8.
+     * @param[in] output           Destination tensor. Data type supported: same as @p input.
+     * @param[in] conv_info        Padding and stride information to use for the convolution.
+     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier);
+
     // Inherited methods overriden:
     void run() override;
 
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
index 1997a90..e4ad97f 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -39,6 +39,159 @@
 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+                          const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(act_info.enabled() && ((input->data_type() != DataType::QASYMM8) || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+                                                                                                         && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+                                                                                                         && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU))),
+                                    "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3);
+    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3);
+
+    const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
+
+    if(biases != nullptr)
+    {
+        if(is_qasymm)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(2));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(output->total_size() != 0)
+    {
+        const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+                                                        GPUTarget gpu_target, std::string &kernel_name)
+{
+    // Output auto inizialitation if not yet initialized
+    const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
+
+    const unsigned int conv_stride_x = conv_info.stride().first;
+    const unsigned int conv_stride_y = conv_info.stride().second;
+    const bool         is_qasymm     = is_data_type_quantized_asymmetric(input->data_type());
+    const bool         is_bifrost    = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
+
+    // Configure kernel window
+    unsigned int num_elems_read_per_iteration_x    = 0;
+    unsigned int num_elems_read_per_iteration_y    = 0;
+    unsigned int num_elems_written_per_iteration_x = 0;
+    unsigned int num_elems_written_per_iteration_y = 0;
+
+    if(input->data_type() == DataType::F16)
+    {
+        kernel_name                       = "depthwise_convolution_3x3_f16";
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+        num_elems_written_per_iteration_y = 1;
+        num_elems_read_per_iteration_y    = 3;
+        switch(conv_stride_x)
+        {
+            case 1:
+                num_elems_read_per_iteration_x = 8;
+                break;
+            case 2:
+                num_elems_read_per_iteration_x = 9;
+                break;
+            case 3:
+                num_elems_read_per_iteration_x = 16;
+                break;
+            default:
+                num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
+                break;
+        }
+        if(is_bifrost)
+        {
+            if(conv_stride_x == 1 && conv_stride_y == 1)
+            {
+                kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16";
+                num_elems_read_per_iteration_x    = 8;
+                num_elems_written_per_iteration_x = 4;
+                num_elems_read_per_iteration_y    = 6;
+                num_elems_written_per_iteration_y = 4;
+            }
+            else if(conv_stride_x == 2 && conv_stride_y == 2)
+            {
+                kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16";
+                num_elems_read_per_iteration_x    = 10;
+                num_elems_written_per_iteration_x = 4;
+                num_elems_read_per_iteration_y    = 5;
+                num_elems_written_per_iteration_y = 2;
+            }
+        }
+    }
+    else if(input->data_type() == DataType::F32 && is_bifrost)
+    {
+        if(conv_stride_x == 1 && conv_stride_y == 1)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32";
+            num_elems_read_per_iteration_x    = 4;
+            num_elems_read_per_iteration_y    = 6;
+            num_elems_written_per_iteration_x = 2;
+            num_elems_written_per_iteration_y = 4;
+        }
+        else if(conv_stride_x == 2 && conv_stride_y == 2)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32";
+            num_elems_read_per_iteration_x    = 6;
+            num_elems_read_per_iteration_y    = 5;
+            num_elems_written_per_iteration_x = 2;
+            num_elems_written_per_iteration_y = 2;
+        }
+        else
+        {
+            kernel_name                       = "depthwise_convolution_3x3";
+            num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+            num_elems_written_per_iteration_y = 1;
+            num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
+            num_elems_read_per_iteration_y    = 3;
+        }
+    }
+    else
+    {
+        kernel_name                       = is_qasymm ? "depthwise_convolution_3x3_quantized_nchw" : "depthwise_convolution_3x3";
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+        num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y < 3) ? (2 / conv_stride_y) : 1;
+        num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
+        num_elems_read_per_iteration_y    = num_elems_written_per_iteration_y + 2;
+    }
+
+    // Create window and update padding
+    Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
+
+    AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(),
+                                       num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
+                                       conv_stride_x, conv_stride_y);
+    AccessWindowStatic    weights_access(weights, 0, 0, 3, 3);
+    AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+
+    bool window_changed = update_window_and_padding(win, input_access, weights_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_pair(err, win);
+}
+} // namespace
+
 CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
     : _conv_stride_x(0), _conv_pad_top(0)
 {
@@ -53,40 +206,10 @@
                                                          unsigned int        depth_multiplier,
                                                          ActivationLayerInfo act_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
 
     bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type());
 
-    if(biases != nullptr)
-    {
-        if(is_qasymm)
-        {
-            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
-        }
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
-
-    // Get convolved dimensions
-    const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
-
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(),
-                       output_shape,
-                       1,
-                       input->info()->data_type(),
-                       input->info()->fixed_point_position(),
-                       input->info()->quantization_info());
-
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-    ARM_COMPUTE_ERROR_ON(output->info()->dimension(2) != weights->info()->dimension(2));
-
     _input         = input;
     _output        = output;
     _weights       = weights;
@@ -98,7 +221,6 @@
     _border_size   = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
 
     // Set build options
-    ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3);
     CLBuildOptions build_opts;
     build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
     build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
@@ -147,109 +269,13 @@
         }
     }
 
-    const GPUTarget gpu_target = get_target();
-    const bool      is_bifrost = gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX);
-
     // Configure kernel window
-    unsigned int num_elems_read_per_iteration_x    = 0;
-    unsigned int num_elems_read_per_iteration_y    = 0;
-    unsigned int num_elems_written_per_iteration_x = 0;
-    unsigned int num_elems_written_per_iteration_y = 0;
+    std::string     kernel_name;
+    const GPUTarget gpu_target = get_target();
 
-    // Create kernel
-    std::string kernel_name;
-
-    if(input->info()->data_type() == DataType::F16)
-    {
-        kernel_name                       = "depthwise_convolution_3x3_f16";
-        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
-        num_elems_written_per_iteration_y = 1;
-        num_elems_read_per_iteration_y    = 3;
-        switch(_conv_stride_x)
-        {
-            case 1:
-                num_elems_read_per_iteration_x = 8;
-                break;
-            case 2:
-                num_elems_read_per_iteration_x = 9;
-                break;
-            case 3:
-                num_elems_read_per_iteration_x = 16;
-                break;
-            default:
-                num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
-                break;
-        }
-        if(is_bifrost)
-        {
-            if(_conv_stride_x == 1 && _conv_stride_y == 1)
-            {
-                kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16";
-                num_elems_read_per_iteration_x    = 8;
-                num_elems_written_per_iteration_x = 4;
-                num_elems_read_per_iteration_y    = 6;
-                num_elems_written_per_iteration_y = 4;
-            }
-            else if(_conv_stride_x == 2 && _conv_stride_y == 2)
-            {
-                kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16";
-                num_elems_read_per_iteration_x    = 10;
-                num_elems_written_per_iteration_x = 4;
-                num_elems_read_per_iteration_y    = 5;
-                num_elems_written_per_iteration_y = 2;
-            }
-        }
-    }
-    else if(input->info()->data_type() == DataType::F32 && is_bifrost)
-    {
-        if(_conv_stride_x == 1 && _conv_stride_y == 1)
-        {
-            kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32";
-            num_elems_read_per_iteration_x    = 4;
-            num_elems_read_per_iteration_y    = 6;
-            num_elems_written_per_iteration_x = 2;
-            num_elems_written_per_iteration_y = 4;
-        }
-        else if(_conv_stride_x == 2 && _conv_stride_y == 2)
-        {
-            kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32";
-            num_elems_read_per_iteration_x    = 6;
-            num_elems_read_per_iteration_y    = 5;
-            num_elems_written_per_iteration_x = 2;
-            num_elems_written_per_iteration_y = 2;
-        }
-        else
-        {
-            kernel_name                       = "depthwise_convolution_3x3";
-            num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
-            num_elems_written_per_iteration_y = 1;
-            num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
-            num_elems_read_per_iteration_y    = 3;
-        }
-    }
-    else
-    {
-        kernel_name                       = is_qasymm ? "depthwise_convolution_3x3_quantized_nchw" : "depthwise_convolution_3x3";
-        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
-        num_elems_written_per_iteration_y = (is_qasymm && _conv_stride_y < 3) ? (2 / _conv_stride_y) : 1;
-        num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
-        num_elems_read_per_iteration_y    = num_elems_written_per_iteration_y + 2;
-    }
-
-    // Create window and update padding
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
-
-    AccessWindowRectangle input_access(input->info(), -_conv_pad_left, -_conv_pad_top,
-                                       num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
-                                       _conv_stride_x, _conv_stride_y);
-    AccessWindowStatic    weights_access(weights->info(), 0, 0, 3, 3);
-    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
-
-    update_window_and_padding(win, input_access, weights_access, output_access);
-
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
 
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
@@ -269,6 +295,17 @@
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }
 
+Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                          unsigned int        depth_multiplier,
+                                                          ActivationLayerInfo act_info, GPUTarget gpu_target)
+{
+    std::string kernel_name;
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name).first);
+
+    return Status{};
+}
+
 void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
index 0aef52f..f44f08b 100644
--- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -42,14 +42,26 @@
 {
 }
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
+{
+    ARM_COMPUTE_UNUSED(conv_info);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && has_bias);
+    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
+
+    return Status{};
+}
+} // namespace
+
 void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
-    ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input->info()->data_type()) && has_bias);
-    ARM_COMPUTE_ERROR_ON((input->info()->dimension(2) * depth_multiplier) != output->info()->dimension(2));
-    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, depth_multiplier));
 
     _input  = input;
     _output = output;
@@ -93,6 +105,13 @@
     ICLKernel::configure(win);
 }
 
+Status CLDepthwiseIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, depth_multiplier));
+
+    return Status{};
+}
+
 void CLDepthwiseIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
index 83fc168..26336eb 100644
--- a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
@@ -34,6 +34,34 @@
 
 using namespace arm_compute;
 
+namespace
+{
+TensorShape compute_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h)
+{
+    TensorShape output_shape(input);
+    output_shape.set(0, conv_w);
+    output_shape.set(1, conv_h);
+    output_shape.set(2, input.x() / (conv_w * conv_h));
+
+    return output_shape;
+}
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32);
+
+    if(output->total_size() != 0)
+    {
+        TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    return Status{};
+}
+} // namespace
+
 CLDepthwiseVectorToTensorKernel::CLDepthwiseVectorToTensorKernel()
     : _input(nullptr), _output(nullptr)
 {
@@ -41,20 +69,13 @@
 
 void CLDepthwiseVectorToTensorKernel::configure(const ICLTensor *input, ICLTensor *output, size_t conv_w, size_t conv_h)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
-    TensorShape output_shape = input->info()->tensor_shape();
-    output_shape.set(0, conv_w);
-    output_shape.set(1, conv_h);
-    output_shape.set(2, input->info()->tensor_shape()[0] / (conv_w * conv_h));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output auto inizialitation if not yet initialized
+    TensorShape output_shape = compute_output_shape(input->info()->tensor_shape(), conv_w, conv_h);
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), conv_w, conv_h));
 
     _input  = input;
     _output = output;
@@ -75,6 +96,12 @@
     ICLKernel::configure(win);
 }
 
+Status CLDepthwiseVectorToTensorKernel::validate(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, conv_w, conv_h));
+    return Status{};
+}
+
 void CLDepthwiseVectorToTensorKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
index 26da96f..b5a607d 100644
--- a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
@@ -34,6 +34,29 @@
 
 using namespace arm_compute;
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (biases != nullptr));
+    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(1));
+    ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(0) * input->dimension(1) + ((biases != nullptr) ? 1 : 0)));
+
+    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(biases->dimension(0) != input->dimension(2));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    return Status{};
+}
+} // namespace
+
 CLDepthwiseWeightsReshapeKernel::CLDepthwiseWeightsReshapeKernel()
     : _input(nullptr), _biases(nullptr), _output(nullptr)
 {
@@ -41,20 +64,8 @@
 
 void CLDepthwiseWeightsReshapeKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *biases)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
-    ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input->info()->data_type()) && (biases != nullptr));
-    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(1));
-    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) + ((biases != nullptr) ? 1 : 0)));
-
-    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(biases->info()->dimension(0) != input->info()->dimension(2));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), (biases != nullptr) ? biases->info() : nullptr));
 
     _input  = input;
     _biases = biases;
@@ -80,6 +91,12 @@
     ICLKernel::configure(win);
 }
 
+Status CLDepthwiseWeightsReshapeKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, biases));
+    return Status{};
+}
+
 void CLDepthwiseWeightsReshapeKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
index a1e47f2..b2ea95b 100644
--- a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
@@ -34,6 +34,42 @@
 
 using namespace arm_compute;
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input0->data_type()) && (output->data_type() != DataType::S32));
+    ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(2) != input1->dimension(1));
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
+{
+    constexpr unsigned int num_elems_read_per_iteration = 4;
+    constexpr unsigned int num_rows_read_per_iteration  = 4;
+
+    const unsigned int border_x = ceil_to_multiple(input0->dimension(0), num_elems_read_per_iteration) - input0->dimension(0);
+    const unsigned int border_y = ceil_to_multiple(input0->dimension(1), num_rows_read_per_iteration) - input0->dimension(1);
+
+    Window win = calculate_max_window(*input0, Steps(num_elems_read_per_iteration));
+
+    AccessWindowRectangle  input0_access(input0, 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration);
+    AccessWindowHorizontal input1_access(input1, 0, num_elems_read_per_iteration);
+    AccessWindowStatic     output_access(output, 0, 0, output->dimension(0) + border_x, output->dimension(1) + border_y);
+
+    bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 CLGEMMMatrixVectorMultiplyKernel::CLGEMMMatrixVectorMultiplyKernel()
     : _input0(nullptr), _input1(nullptr), _output(nullptr), _num_rows_read_per_iteration(0), _border_size(0)
 {
@@ -45,11 +81,8 @@
 
 void CLGEMMMatrixVectorMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
-    ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input0->info()->data_type()) && (output->info()->data_type() != DataType::S32));
-    ARM_COMPUTE_ERROR_ON(input0->info()->dimension(2) != 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;
@@ -93,17 +126,17 @@
 
     _border_size = BorderSize(border_y, border_x);
 
-    Window win = calculate_max_window(*input0->info(), Steps(num_elems_read_per_iteration));
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+}
 
-    AccessWindowRectangle  input0_access(input0->info(), 0, 0, num_elems_read_per_iteration, _num_rows_read_per_iteration);
-    AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_read_per_iteration);
-    AccessWindowStatic     output_access(_output->info(), 0, 0, _output->info()->dimension(0) + border_x, _output->info()->dimension(1) + border_y);
+Status CLGEMMMatrixVectorMultiplyKernel::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(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first);
 
-    update_window_and_padding(win, input0_access, input1_access, output_access);
-
-    _output->info()->set_valid_region(ValidRegion(Coordinates(), _output->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMMatrixVectorMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 88bb0c4..676a121 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -24,6 +24,8 @@
 #include "arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h"
 
 #include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h"
+#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h"
 #include "arm_compute/core/PixelValue.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
@@ -66,6 +68,21 @@
     _border_handler.configure(input, _kernel->border_size(), BorderMode::CONSTANT, zero_value);
 }
 
+Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                unsigned int        depth_multiplier,
+                                                ActivationLayerInfo act_info, GPUTarget gpu_target)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW && input->data_layout() != DataLayout::NHWC);
+
+    if(input->data_layout() == DataLayout::NCHW)
+    {
+        return CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target);
+    }
+
+    return CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info);
+}
+
 void CLDepthwiseConvolutionLayer3x3::run()
 {
     CLScheduler::get().enqueue(_border_handler);
@@ -82,7 +99,6 @@
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON((input->info()->dimension(2) * depth_multiplier) != weights->info()->dimension(2));
 
     const size_t weights_w = weights->info()->dimension(0);
     const size_t weights_h = weights->info()->dimension(1);
@@ -168,6 +184,53 @@
     _v2mm_output.allocator()->allocate();
 }
 
+Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                             unsigned int depth_multiplier)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != weights->dimension(2));
+
+    const bool         is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+    const bool         append_bias  = (biases != nullptr) && !is_quantized;
+    const TensorShape  output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+    const size_t       weights_w    = weights->dimension(0);
+    const size_t       weights_h    = weights->dimension(1);
+    const size_t       weights_z    = weights->dimension(2);
+    const unsigned int conv_w       = output_shape.x();
+    const unsigned int conv_h       = output_shape.y();
+    const size_t       patch_size   = weights_w * weights_h + ((append_bias) ? 1 : 0);
+    const size_t       conv_size    = conv_w * conv_h;
+
+    TensorShape shape_im2col = input->tensor_shape();
+    shape_im2col.set(0, patch_size);
+    shape_im2col.set(1, conv_size);
+    shape_im2col.set(2, weights_z);
+    TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier));
+
+    const TensorShape shape_weights_reshape(patch_size, weights_z);
+    TensorInfo        weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseWeightsReshapeKernel::validate(weights, &weights_reshaped, append_bias ? biases : nullptr));
+
+    DataType    v2mm_dt        = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
+    TensorShape shape_v2mm_out = input->tensor_shape();
+    shape_v2mm_out.set(0, conv_size * weights_z);
+    shape_v2mm_out.set(1, 1);
+    shape_v2mm_out.set(2, 1);
+    TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output));
+
+    TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output, conv_w, conv_h));
+
+    if(is_quantized)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output));
+    }
+
+    return Status{};
+}
+
 void CLDepthwiseConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)
diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
index 54b7925..093d342 100644
--- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
@@ -53,6 +53,171 @@
 TEST_SUITE(CL)
 TEST_SUITE(DepthwiseConvolutionLayer)
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+               framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Mismatching data type input/weights
+                                                       TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32, 0),     // Mismatching input feature maps
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Unsupported weights dimensions
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::QASYMM8, 0), // Unsupported activation
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Mismatching depth multiplier
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Invalid stride
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Invalid biases size
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Invalid biases dimensions
+                                                       TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),     // Invalid output size
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),     // Window shrink
+                                                       TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(50U, 32U, 8U), 1, DataType::QASYMM8, 0),
+                                                     }),
+               framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(5U, 5U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::QASYMM8, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8, 0),
+                                                       })),
+               framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::S32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(24U), 1, DataType::S32, 0),
+                                                      })),
+               framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::QASYMM8, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(48U, 30U, 24U), 1, DataType::QASYMM8, 0),
+                                                      })),
+               framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(4, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                      PadStrideInfo(1, 1, 0, 0),
+                                                     })),
+               framework::dataset::make("DepthMultiplier", { 1,
+                                                             1,
+                                                             1,
+                                                             1,
+                                                             3,
+                                                             1,
+                                                             1,
+                                                             1,
+                                                             1,
+                                                             1,
+                                                             2,
+                                                             3,
+                                                            })),
+                framework::dataset::make("ActivationInfo", { ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(),
+                                                             ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                           })),
+               framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true, true })),
+               input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, act_info, expected)
+{
+    bool is_valid = bool(CLDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier, act_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+
+DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+                framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),    // Mismatching data type input/weights
+                                                        TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32, 0),    // Mismatching input feature maps
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),    // Mismatching depth multiplier
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),    // Invalid biases size
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),    // Invalid biases dimensions
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),    // Invalid output size
+                                                        TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8, 0),
+                                                      }),
+                framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32, 0),
+                                                          TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8, 0),
+                                                        })),
+                framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(2U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(24U), 1, DataType::S32, 0),
+                                                       })),
+                framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32, 0),
+                                                         TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8, 0),
+                                                       })),
+                framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 0, 0),
+                                                       PadStrideInfo(1, 1, 1, 0),
+                                                      })),
+                framework::dataset::make("DepthMultiplier", { 1,
+                                                              1,
+                                                              3,
+                                                              1,
+                                                              1,
+                                                              1,
+                                                              2,
+                                                              3,
+                                                             })),
+                framework::dataset::make("Expected", { false, false, false, false, false, false, true, true })),
+                input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected)
+{
+    bool is_valid = bool(CLDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using CLDepthwiseConvolutionLayerFixture = DepthwiseConvolutionLayerValidationFixture<CLTensor, CLAccessor, CLDepthwiseConvolutionLayer, T>;