diff --git a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
index 7741f12..6886f54 100644
--- a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
+++ b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
@@ -33,8 +33,55 @@
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
+                                                         DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+                                                         DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_interleaved_shape(*input));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+    unsigned int           num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input->data_type());
+    constexpr unsigned int num_elems_processed_per_iteration_y = 4;
+    const unsigned int     num_elems_written_per_iteration     = num_elems_processed_per_iteration_x * num_elems_processed_per_iteration_y;
+    bool                   window_changed                      = false;
+
+    // Configure kernel window
+    Window                win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+    window_changed = window_changed || update_window_and_padding(win, input_access);
+
+    // Configure window in case of configured output
+    if(output->total_size() != 0)
+    {
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration, 1, 4.f, 0.25f);
+        window_changed = window_changed || update_window_and_padding(win, output_access);
+        output_access.set_valid_region(win, input->valid_region());
+    }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
 
 CLGEMMInterleave4x4Kernel::CLGEMMInterleave4x4Kernel()
     : _input(nullptr), _output(nullptr)
@@ -43,22 +90,13 @@
 
 void CLGEMMInterleave4x4Kernel::configure(const ICLTensor *input, ICLTensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
-                                                  DataType::U16, DataType::S16, DataType::QS16,
-                                                  DataType::U32, DataType::S32,
-                                                  DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
-    TensorShape output_shape = input->info()->tensor_shape();
-    output_shape.set(0, input->info()->dimension(0) * 4);
-    output_shape.set(1, std::ceil(input->info()->dimension(1) / 4.0f));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_interleaved_shape(*input->info())));
 
-    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);
+    // Perform validate step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
 
     _input  = input;
     _output = output;
@@ -68,20 +106,9 @@
     _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name));
 
     // Configure kernel window
-    const unsigned int     num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input->info()->data_type());
-    constexpr unsigned int num_elems_processed_per_iteration_y = 4;
-    const unsigned int     num_elems_written_per_iteration     = num_elems_processed_per_iteration_x * num_elems_processed_per_iteration_y;
-
-    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-    AccessWindowRectangle input_access(input->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration, 1, 4.f, 0.25f);
-
-    update_window_and_padding(win, input_access, output_access);
-
-    output_access.set_valid_region(win, input->info()->valid_region());
-
-    ICLKernel::configure(win);
+    auto win_config = validate_and_configure_window(input->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
 
     // Set config_id for enabling LWS tuning
     _config_id = "interleave4x4_";
@@ -92,6 +119,14 @@
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }
 
+Status CLGEMMInterleave4x4Kernel::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
+
+    return Status{};
+}
+
 void CLGEMMInterleave4x4Kernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
index 1d9fe4b..423592b 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
@@ -46,6 +46,76 @@
 class Coordinates;
 } // namespace arm_compute
 
+namespace
+{
+using ElementsProcessed = Steps;
+
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+    if(!is_interleaved_transposed)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, bool is_interleaved_transposed,
+                                                        ElementsProcessed &num_elements_processed)
+{
+    unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
+    unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+
+    Window win{};
+    bool   window_changed = false;
+
+    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
+    if(is_interleaved_transposed)
+    {
+        // Configure window
+        num_elems_processed_per_iteration_x                        = 16;
+        num_elems_processed_per_iteration_y                        = 4;
+        constexpr unsigned int num_elems_read_per_iteration_input0 = 4;
+        constexpr unsigned int num_elems_read_per_iteration_input1 = 16;
+
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowRectangle input0_access(input0, 0, 0, num_elems_read_per_iteration_input0, 1);
+        AccessWindowRectangle input1_access(input1, 0, 0, num_elems_read_per_iteration_input1, 1);
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
+    }
+    else
+    {
+        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x
+        num_elems_processed_per_iteration_x = 16;
+        num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+
+        // Configure window
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowStatic    input0_access(input0, 0, 0, input0->dimension(0), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
+        AccessWindowStatic    input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        Coordinates coord;
+        coord.set_num_dimensions(output->num_dimensions());
+        output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
+    }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 CLGEMMLowpMatrixMultiplyKernel::CLGEMMLowpMatrixMultiplyKernel()
     : _input0(nullptr), _input1(nullptr), _output(nullptr)
 {
@@ -53,72 +123,37 @@
 
 void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, bool is_interleaved_transposed)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
-
-    if(!is_interleaved_transposed)
-    {
-        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(), is_interleaved_transposed));
 
     _input0 = input0;
     _input1 = input1;
     _output = output;
 
-    CLBuildOptions build_opts;
+    ElementsProcessed num_elements_processed{};
 
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, num_elements_processed);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+
+    // Create build options
+    CLBuildOptions build_opts;
+    std::string    kernel_name(" ");
     if(is_interleaved_transposed)
     {
-        // Create kernel and set static arguments
         build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0)));
-        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_mm_interleaved_transposed", build_opts.options()));
-
-        // Configure window
-        constexpr unsigned int num_elems_processed_per_iteration_x = 16;
-        constexpr unsigned int num_elems_processed_per_iteration_y = 4;
-        constexpr unsigned int num_elems_read_per_iteration_input0 = 4;
-        constexpr unsigned int num_elems_read_per_iteration_input1 = 16;
-
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-        AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_read_per_iteration_input0, 1);
-        AccessWindowRectangle input1_access(input1->info(), 0, 0, num_elems_read_per_iteration_input1, 1);
-        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
-        update_window_and_padding(win, input0_access, input1_access, output_access);
-
-        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
-
-        ICLKernel::configure(win);
+        kernel_name = "gemmlowp_mm_interleaved_transposed";
     }
     else
     {
-        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x
-        constexpr unsigned int num_elems_processed_per_iteration_x = 16;
-        const unsigned int     num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
-
         build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
-        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elems_processed_per_iteration_x));
-        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elems_processed_per_iteration_y));
-
-        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_mm", build_opts.options()));
-
-        // Configure window
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-        AccessWindowStatic    input0_access(input0->info(), 0, 0, input0->info()->dimension(0), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y));
-        AccessWindowStatic    input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
-        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
-        update_window_and_padding(win, input0_access, input1_access, output_access);
-
-        Coordinates coord;
-        coord.set_num_dimensions(output->info()->num_dimensions());
-        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
-
-        ICLKernel::configure(win);
+        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x()));
+        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
+        kernel_name = "gemmlowp_mm";
     }
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // Set config_id for enabling LWS tuning
     _config_id = "gemmlowp_";
@@ -132,6 +167,20 @@
     _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
 }
 
+Status CLGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed)
+{
+    ElementsProcessed num_elements_processed{};
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
+                                                              input1->clone().get(),
+                                                              output->clone().get(),
+                                                              is_interleaved_transposed,
+                                                              num_elements_processed)
+                                .first);
+
+    return Status{};
+}
+
 void CLGEMMLowpMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
index 2877a74..d05939f 100644
--- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
@@ -44,6 +44,81 @@
 class Coordinates;
 } // namespace arm_compute
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
+                          int32_t a_offset, int32_t b_offset)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
+
+    // If a_offset == 0, vector_sum_col can be a nullptr
+    if(a_offset != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
+    }
+
+    // If b_offset == 0, vector_sum_row can be a nullptr
+    if(b_offset != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->dimension(0) != mm_result->dimension(1));
+
+        TensorShape output_shape = mm_result->tensor_shape();
+        if(output_shape.num_dimensions() > 1)
+        {
+            TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
+            vector_sum_row_shape.collapse_from(1);
+            output_shape.collapse_from(2);
+
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[2],
+                                            "mm_result tensor must have the same number of batches of output tensor");
+
+            if(a_offset != 0)
+            {
+                TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
+                vector_sum_col_shape.collapse_from(1);
+
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
+                                                "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
+            }
+        }
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row,
+                                                        int32_t a_offset, int32_t b_offset)
+{
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    bool                   window_changed                    = false;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal mm_result_access(mm_result, 0, num_elems_processed_per_iteration);
+    window_changed = window_changed || update_window_and_padding(win,
+                                                                 mm_result_access);
+
+    if(a_offset != 0)
+    {
+        AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration);
+        window_changed = window_changed || update_window_and_padding(win,
+                                                                     vector_sum_col_access);
+    }
+    if(b_offset != 0)
+    {
+        AccessWindowStatic vector_sum_row_access(vector_sum_row, 0, 0, vector_sum_row->dimension(0), 0); // NOLINT
+        window_changed = window_changed || update_window_and_padding(win,
+                                                                     vector_sum_row_access);
+    }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 CLGEMMLowpOffsetContributionKernel::CLGEMMLowpOffsetContributionKernel()
     : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr)
 {
@@ -51,7 +126,16 @@
 
 void CLGEMMLowpOffsetContributionKernel::configure(ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
+                                                  vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
+                                                  vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
+                                                  a_offset, b_offset)); // NOLINT
+
+    _vector_sum_col = vector_sum_col;
+    _vector_sum_row = vector_sum_row;
+    _mm_result      = mm_result;
 
     // Set the arguments to pass at compile time
     CLBuildOptions build_opts;
@@ -59,74 +143,36 @@
     // If a_offset == 0, vector_sum_col can be a nullptr
     if(a_offset != 0)
     {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
-        ARM_COMPUTE_ERROR_ON(vector_sum_col->info()->dimension(0) != mm_result->info()->dimension(0));
-
         build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
         build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
     }
-
     // If b_offset == 0, vector_sum_row can be a nullptr
-    if(b_offset != 0)
-    {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
-        ARM_COMPUTE_ERROR_ON(vector_sum_row->info()->dimension(0) != mm_result->info()->dimension(1));
-
-        // Validate batches
-        TensorShape output_shape = mm_result->info()->tensor_shape();
-        if(output_shape.num_dimensions() > 1)
-        {
-            TensorShape vector_sum_row_shape = vector_sum_row->info()->tensor_shape();
-            vector_sum_row_shape.collapse_from(1);
-            output_shape.collapse_from(2);
-
-            ARM_COMPUTE_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[2], "mm_result tensor must have the same number of batches of output tensor");
-
-            if(a_offset != 0)
-            {
-                TensorShape vector_sum_col_shape = vector_sum_col->info()->tensor_shape();
-                vector_sum_col_shape.collapse_from(1);
-
-                ARM_COMPUTE_ERROR_ON_MSG(vector_sum_col_shape[1] != 1
-                                         && vector_sum_col_shape[1] != vector_sum_row_shape[1],
-                                         "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
-            }
-        }
-
-        build_opts.add_option("-DB_OFFSET=" + support::cpp11::to_string(b_offset));
-    }
-
+    build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
     build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * k));
 
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_offset_contribution", build_opts.options()));
 
-    _vector_sum_col = vector_sum_col;
-    _vector_sum_row = vector_sum_row;
-    _mm_result      = mm_result;
-
-    constexpr unsigned int num_elems_processed_per_iteration = 16;
-
     // Configure kernel window
-    Window win = calculate_max_window(*mm_result->info(), Steps(num_elems_processed_per_iteration));
+    auto win_config = validate_and_configure_window(mm_result->info(),
+                                                    vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
+                                                    vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
+                                                    a_offset, b_offset); // NOLINT
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+}
 
-    AccessWindowHorizontal mm_result_access(mm_result->info(), 0, num_elems_processed_per_iteration);
+Status CLGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
+                                                    int32_t a_offset, int32_t b_offset)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(),
+                                                              vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
+                                                              vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
+                                                              a_offset, b_offset)
+                                .first); // NOLINT
 
-    update_window_and_padding(win, mm_result_access);
-
-    if(a_offset != 0)
-    {
-        AccessWindowHorizontal vector_sum_col_access(vector_sum_col->info(), 0, num_elems_processed_per_iteration);
-        update_window_and_padding(win, vector_sum_col_access);
-    }
-
-    if(b_offset != 0)
-    {
-        AccessWindowStatic vector_sum_row_access(vector_sum_row->info(), 0, 0, vector_sum_row->info()->dimension(0), 0);
-        update_window_and_padding(win, vector_sum_row_access);
-    }
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMLowpOffsetContributionKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
index bcf04b0..6951512 100644
--- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
@@ -44,6 +44,59 @@
 class Coordinates;
 } // namespace arm_compute
 
+namespace
+{
+Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+
+    return Status{};
+}
+std::pair<Status, Window> validate_and_configure_window_matrix_a_reduction(ITensorInfo *input, ITensorInfo *output)
+{
+    const unsigned int num_elems_processed_per_iteration = 1;
+
+    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic     input_access(input, 0, 0, ceil_to_multiple(input->dimension(0), 16), input->dimension(1));
+    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+
+Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window_matrix_b_reduction(ITensorInfo *input, ITensorInfo *output)
+{
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic     input_access(input, 0, 0, ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration), input->dimension(1));
+    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 ICLGEMMLowpReductionKernel::ICLGEMMLowpReductionKernel()
     : _input(), _output()
 {
@@ -51,8 +104,9 @@
 
 void CLGEMMLowpMatrixAReductionKernel::configure(const ICLTensor *mtx_a, ICLTensor *vector_sum_row)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mtx_a, 1, DataType::QASYMM8);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a->info(), vector_sum_row->info()));
 
     _input  = mtx_a;
     _output = vector_sum_row;
@@ -64,21 +118,18 @@
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_matrix_a_reduction", build_opts.options()));
 
-    const unsigned int num_elems_processed_per_iteration = 1;
-
     // Configure kernel window
-    Window win = calculate_max_window(*_output->info(), Steps(num_elems_processed_per_iteration));
+    auto win_config = validate_and_configure_window_matrix_a_reduction(_input->info(), _output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+}
 
-    AccessWindowStatic     input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), 16), _input->info()->dimension(1));
-    AccessWindowHorizontal output_access(_output->info(), 0, num_elems_processed_per_iteration);
+Status CLGEMMLowpMatrixAReductionKernel::validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_matrix_a_reduction(mtx_a->clone().get(), vector_sum_row->clone().get()).first);
 
-    update_window_and_padding(win,
-                              input_access,
-                              output_access);
-
-    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), _output->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMLowpMatrixAReductionKernel::run(const Window &window, cl::CommandQueue &queue)
@@ -107,8 +158,8 @@
 
 void CLGEMMLowpMatrixBReductionKernel::configure(const ICLTensor *mtx_b, ICLTensor *vector_sum_col)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mtx_b, 1, DataType::QASYMM8);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b->info(), vector_sum_col->info()));
 
     _input  = mtx_b;
     _output = vector_sum_col;
@@ -121,21 +172,18 @@
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_matrix_b_reduction", build_opts.options()));
 
-    constexpr unsigned int num_elems_processed_per_iteration = 16;
-
     // Configure kernel window
-    Window win = calculate_max_window(*vector_sum_col->info(), Steps(num_elems_processed_per_iteration));
+    auto win_config = validate_and_configure_window_matrix_b_reduction(_input->info(), _output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+}
 
-    AccessWindowStatic     input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), num_elems_processed_per_iteration), _input->info()->dimension(1));
-    AccessWindowHorizontal output_access(_output->info(), 0, num_elems_processed_per_iteration);
+Status CLGEMMLowpMatrixBReductionKernel::validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_matrix_b_reduction(mtx_b->clone().get(), vector_sum_col->clone().get()).first);
 
-    update_window_and_padding(win,
-                              input_access,
-                              output_access);
-
-    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), _output->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMLowpMatrixBReductionKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
index 015b4f7..d5c93dd 100644
--- a/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
@@ -36,6 +36,37 @@
 
 using namespace arm_compute;
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(biases, accum);
+    ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() != 1);
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *accum, ITensorInfo *biases, GPUTarget gpu_target,
+                                                        unsigned int &num_elems_processed_per_iteration)
+{
+    // Select the vector size to use (8 for Bifrost; 16 for Midgard).
+    num_elems_processed_per_iteration = (gpu_target == GPUTarget::BIFROST) ? 8 : 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*accum, Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic     biases_access(biases, 0, 0, ceil_to_multiple(biases->dimension(0), num_elems_processed_per_iteration), biases->dimension(1));
+    AccessWindowHorizontal accum_access(accum, 0, num_elems_processed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win, biases_access, accum_access);
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 CLGEMMMatrixAccumulateBiasesKernel::CLGEMMMatrixAccumulateBiasesKernel()
     : _accum(nullptr), _biases(nullptr)
 {
@@ -43,18 +74,21 @@
 
 void CLGEMMMatrixAccumulateBiasesKernel::configure(ICLTensor *accum, const ICLTensor *biases)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(biases, accum);
-    ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() != 1);
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(accum, biases);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(accum->info(), biases->info()));
 
     _biases = biases;
     _accum  = accum;
 
     // Get the target architecture
-    GPUTarget arch_target = get_arch_from_target(get_target());
-    // Select the vector size to use (8 for Bifrost; 16 for Midgard).
-    const unsigned int vector_size = (arch_target == GPUTarget::BIFROST) ? 8 : 16;
+    GPUTarget    arch_target = get_arch_from_target(get_target());
+    unsigned int vector_size = 0;
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(accum->info(), biases->info(), arch_target, vector_size);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
 
     // Add build options
     CLBuildOptions build_opts;
@@ -65,18 +99,15 @@
 
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_accumulate_biases", build_opts.options()));
+}
 
-    // Configure kernel window
-    const unsigned int num_elems_processed_per_iteration = vector_size;
+Status CLGEMMMatrixAccumulateBiasesKernel::validate(const ITensorInfo *accum, const ITensorInfo *biases, GPUTarget gpu_target)
+{
+    unsigned int num_elems_processed_per_iteration = 0;
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(accum, biases));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(accum->clone().get(), biases->clone().get(), gpu_target, num_elems_processed_per_iteration).first);
 
-    Window win = calculate_max_window(*_accum->info(), Steps(num_elems_processed_per_iteration));
-
-    AccessWindowStatic     biases_access(biases->info(), 0, 0, ceil_to_multiple(biases->info()->dimension(0), num_elems_processed_per_iteration), biases->info()->dimension(1));
-    AccessWindowHorizontal accum_access(_accum->info(), 0, num_elems_processed_per_iteration);
-
-    update_window_and_padding(win, biases_access, accum_access);
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMMatrixAccumulateBiasesKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index 16706dd..f51d0f9 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -42,6 +42,81 @@
 
 using namespace arm_compute;
 
+namespace
+{
+using ElementsProcessed = Steps;
+
+inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+    if(!is_interleaved_transposed)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+    }
+
+    return Status{};
+}
+
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output,
+                                                               bool is_interleaved_transposed, GPUTarget gpu_target,
+                                                               ElementsProcessed &num_elements_processed)
+{
+    bool   window_changed = false;
+    Window win{};
+
+    const DataType data_type                           = input0->data_type();
+    unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
+    unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+
+    if(is_interleaved_transposed)
+    {
+        // Configure kernel window
+        num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
+        num_elems_processed_per_iteration_y = 4;
+
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
+        AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
+    }
+    else // The input tensors have not been reshaped
+    {
+        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
+        num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
+        num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+
+        // Create kernels according to the architecture, data type and input size.
+        if(gpu_target == GPUTarget::BIFROST && data_type == DataType::F32)
+        {
+            num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000) ? 2 : 4;
+        }
+
+        // Configure window
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowStatic    input0_access(input0, 0, 0, input0->dimension(0), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
+        AccessWindowStatic    input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        Coordinates coord;
+        coord.set_num_dimensions(output->num_dimensions());
+        output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
+    }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
 CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel()
     : _input0(nullptr), _input1(nullptr), _output(nullptr)
 {
@@ -49,13 +124,10 @@
 
 void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, bool is_interleaved_transposed)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
-    if(!is_interleaved_transposed)
-    {
-        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+
+    // Perform validate step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed));
 
     _input0 = input0;
     _input1 = input1;
@@ -82,14 +154,19 @@
         _lws_hint = cl::NDRange(8, 8);
     }
 
+    ElementsProcessed num_elements_processed{};
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, arch_target, num_elements_processed);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+
     // Create build options
     CLBuildOptions build_opts;
     build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(fp_pos));
 
-    const bool multiply_alpha = std::abs(1.0f - alpha) > 0.00001f;
-
     // Only define ALPHA when alpha is not 1.0f. This avoids performing unnecessary multiplications.
-    if(multiply_alpha)
+    if(std::abs(1.0f - alpha) > 0.00001f)
     {
         build_opts.add_option_if_else(is_data_type_fixed_point(data_type),
                                       "-DALPHA=" + support::cpp11::to_string((data_type == DataType::QS8 ? sqcvt_qs8_f32(alpha, fp_pos) : sqcvt_qs16_f32(alpha, fp_pos))),
@@ -108,49 +185,19 @@
         {
             kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type));
         }
-
-        // Configure kernel window
-        const unsigned int     num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
-        constexpr unsigned int num_elems_processed_per_iteration_y = 4;
-
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-        AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
-        AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
-        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
-        update_window_and_padding(win, input0_access, input1_access, output_access);
-
-        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
-
-        ICLKernel::configure(win);
     }
     else // The input tensors have not been reshaped
     {
         build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
 
-        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
-        unsigned int       num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
-        const unsigned int num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
-
         // Create kernels according to the architecture, data type and input size.
         if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
         {
             // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
             // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
             // FC6 and FC7 of AlexNet and VGG-16).
-            if(input1->info()->dimension(0) <= 1000)
-            {
-                // Each work-item processes 2 elements in the X dimension.
-                num_elems_processed_per_iteration_x = 2;
-                kernel_name                         = "gemm_mm_floating_point_f32_bifrost_1000";
-            }
-            else
-            {
-                // Each work-item processes 4 elements in the X dimension (as in the default case).
-                num_elems_processed_per_iteration_x = 4;
-                kernel_name                         = "gemm_mm_floating_point_f32_bifrost";
-            }
+            kernel_name = (input1->info()->dimension(0) <= 1000) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost";
+
             // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
             // via exhaustive autotuning over a range of representative layer configurations.
             _lws_hint = cl::NDRange(4);
@@ -164,23 +211,8 @@
             build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
             kernel_name = "gemm_mm_floating_point";
         }
-        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elems_processed_per_iteration_y));
-        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elems_processed_per_iteration_x));
-
-        // Configure window
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-        AccessWindowStatic    input0_access(input0->info(), 0, 0, input0->info()->dimension(0), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y));
-        AccessWindowStatic    input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
-        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
-
-        update_window_and_padding(win, input0_access, input1_access, output_access);
-
-        Coordinates coord;
-        coord.set_num_dimensions(output->info()->num_dimensions());
-        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
-
-        ICLKernel::configure(win);
+        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
+        build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x()));
     }
 
     // Create kernel
@@ -198,6 +230,22 @@
     _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
 }
 
+Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, GPUTarget gpu_target)
+{
+    ElementsProcessed num_elements_processed{};
+    ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
+                                                              input1->clone().get(),
+                                                              output->clone().get(),
+                                                              is_interleaved_transposed,
+                                                              gpu_target,
+                                                              num_elements_processed)
+                                .first);
+
+    return Status{};
+}
+
 void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
diff --git a/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp b/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
index 35074f9..69a545b 100644
--- a/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
@@ -33,36 +33,82 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 
 #include <cmath>
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
+                                                         DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+                                                         DataType::F16, DataType::F32);
+
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(),
+                                                           compute_transpose1xW_with_element_size_shape(*input));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int &num_elems_processed_per_iteration)
+{
+    num_elems_processed_per_iteration = 16 / input->element_size();
+
+    const int scale_x        = num_elems_processed_per_iteration;
+    bool      window_changed = false;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+    if((win.x().end() / scale_x) == 0)
+    {
+        return std::make_pair(ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Transposed shape would be 0 in the second dimension"), win);
+    }
+
+    AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+    window_changed = window_changed || update_window_and_padding(win, input_access);
+
+    // Configure window in case of configured output
+    if(output->total_size() != 0)
+    {
+        AccessWindowTranspose output_access(output, 0, 0, num_elems_processed_per_iteration, 1, scale_x, 1.f / scale_x);
+        window_changed = window_changed || update_window_and_padding(win, output_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), input->tensor_shape()));
+    }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
 
 void CLGEMMTranspose1xWKernel::configure(const ICLTensor *input, ICLTensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
-                                                  DataType::U16, DataType::S16, DataType::QS16,
-                                                  DataType::U32, DataType::S32, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
-    TensorShape  output_shape{ input->info()->tensor_shape() };
-    const size_t transpose_w = 16 / input->info()->element_size();
-    output_shape.set(0, input->info()->dimension(1) * transpose_w);
-    output_shape.set(1, static_cast<size_t>(std::ceil((input->info()->dimension(0) / static_cast<float>(transpose_w)))));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output tensor auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(*input->info())));
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
-
-    const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
-    const int          scale_x                           = num_elems_processed_per_iteration;
+    // Perform validate step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
 
     _input  = input;
     _output = output;
 
+    // Configure kernel window
+    unsigned int num_elems_processed_per_iteration = 1;
+    auto         win_config                        = validate_and_configure_window(input->info(), output->info(), num_elems_processed_per_iteration);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+
     /*
      * Following an example of how the transposition1xW works when the input data type is F32
      *
@@ -76,20 +122,15 @@
     // Create kernel
     std::string kernel_name = "gemm_transpose1x" + support::cpp11::to_string(num_elems_processed_per_iteration);
     _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name));
+}
 
-    // Configure window
-    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+Status CLGEMMTranspose1xWKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    unsigned int num_elems_processed_per_iteration = 1;
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), num_elems_processed_per_iteration).first);
 
-    ARM_COMPUTE_ERROR_ON_MSG((win.x().end() / scale_x) == 0, "Transposed shape would be 0 in the second dimension");
-
-    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
-    AccessWindowTranspose  output_access(output->info(), 0, 0, num_elems_processed_per_iteration, 1, scale_x, 1.f / scale_x);
-
-    update_window_and_padding(win, input_access, output_access);
-
-    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), input->info()->tensor_shape()));
-
-    ICLKernel::configure(win);
+    return Status{};
 }
 
 void CLGEMMTranspose1xWKernel::run(const Window &window, cl::CommandQueue &queue)
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 6514d6c..0e9f2c5 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -39,6 +39,24 @@
 
 using namespace arm_compute;
 
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+
+    // Checks performed when output is configured
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    return Status{};
+}
+} // namespace
+
 CLIm2ColKernel::CLIm2ColKernel()
     : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr)
 {
@@ -46,9 +64,10 @@
 
 void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, 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_NULLPTR(input, output);
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
 
     _input  = input;
     _output = output;
@@ -184,6 +203,15 @@
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }
 
+Status CLIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+{
+    ARM_COMPUTE_UNUSED(kernel_dims);
+    ARM_COMPUTE_UNUSED(conv_info);
+    ARM_COMPUTE_UNUSED(has_bias);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    return Status{};
+}
+
 void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON(_run_func == nullptr);