diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp
index 566b75a..3bb6d6f 100644
--- a/tests/validation/NEON/DeconvolutionLayer.cpp
+++ b/tests/validation/NEON/DeconvolutionLayer.cpp
@@ -58,6 +58,106 @@
 TEST_SUITE(NEON)
 TEST_SUITE(DeconvolutionLayer)
 
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, (combine(datasets::SmallDeconvolutionShapes(), framework::dataset::make("DataType", DataType::F32))),
+               input_shape, data_type)
+{
+    // Create shapes
+    const unsigned int kernel_size_x = 3;
+    const unsigned int kernel_size_y = 3;
+    const unsigned int num_kernels   = 1;
+    const TensorShape  weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
+    const TensorShape  bias_shape(num_kernels);
+    auto               out_dim      = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, 1, 1, 0, 0, 1, 1);
+    TensorShape        output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
+
+    // Create tensors
+    Tensor src     = create_tensor<Tensor>(input_shape, data_type, 1);
+    Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1);
+    Tensor bias    = create_tensor<Tensor>(bias_shape, data_type, 1);
+    Tensor dst     = create_tensor<Tensor>(output_shape, data_type, 1);
+
+    ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    NEDeconvolutionLayer deconv;
+    deconv.configure(&src, &weights, &bias, &dst, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), 0, 0);
+
+    // Validate valid region
+    const ValidRegion src_valid_region     = shape_to_valid_region(input_shape);
+    const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape);
+    const ValidRegion bias_valid_region    = shape_to_valid_region(bias_shape);
+    const ValidRegion dst_valid_region     = shape_to_valid_region(output_shape);
+
+    validate(src.info()->valid_region(), src_valid_region);
+    validate(weights.info()->valid_region(), weights_valid_region);
+    validate(bias.info()->valid_region(), bias_valid_region);
+    validate(dst.info()->valid_region(), dst_valid_region);
+}
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),   // Mismatching data type
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),   // Invalid weights shape
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 4),   // Non supported data type
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 11),  // Invalid bias shape
+                                            TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32, 0), // Window shrink
+                                            TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+                                          }),
+    framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::QS8, 5),
+                                            TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32, 11),
+                                            TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32, 0),
+                                              TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("BiasInfo",  { TensorInfo(TensorShape(1U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 5),
+                                            TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, 11),
+                                            TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 5),
+                                            TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 1, 1),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                           })),
+    framework::dataset::make("ax",          {   1U,
+                                                1U,
+                                                1U,
+                                                1U,
+                                                0U,
+                                                0U,
+                                            })),
+   framework::dataset::make("ay",           {   1U,
+                                                1U,
+                                                1U,
+                                                1U,
+                                                0U,
+                                                0U,
+                                            })),
+    framework::dataset::make("Expected", { false, false, false, false, false, true })),
+    input_info, weights_info, bias_info, output_info, pad_info, ax, ay, expected)
+{
+    bool is_valid = bool(NEDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_info, ax, ay));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NEDeconvolutionLayerFixture4x4 = DeconvolutionValidationFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 4, 4>;
 
diff --git a/tests/validation/NEON/LocallyConnected.cpp b/tests/validation/NEON/LocallyConnected.cpp
index 56430d9..b00f274 100644
--- a/tests/validation/NEON/LocallyConnected.cpp
+++ b/tests/validation/NEON/LocallyConnected.cpp
@@ -48,6 +48,67 @@
 TEST_SUITE(NEON)
 TEST_SUITE(LocallyConnected)
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+    framework::dataset::make("InputInfo",  { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/weights
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/bias
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/output
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Asymmetric padding
+                                             TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0)
+                                           }),
+    framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("BiasInfo",   { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 274U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
+                                             TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0)
+                                           })),
+    framework::dataset::make("PadStride",  { PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 0, 0),
+                                             PadStrideInfo(2, 1, 1, 0, 0, 0, DimensionRoundingType::FLOOR),
+                                             PadStrideInfo(2, 1, 0, 0)
+                                           })),
+    framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
+    input_info, weights_info, bias_info, output_info, conv_info, expected)
+{
+    bool is_valid = bool(NELocallyConnectedLayer::validate(&input_info.clone()->set_is_resizable(false),
+                                                           &weights_info.clone()->set_is_resizable(false),
+                                                           &bias_info.clone()->set_is_resizable(false),
+                                                           &output_info.clone()->set_is_resizable(false),
+                                                           conv_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallLocallyConnectedDataset(), datasets::LargeLocallyConnectedDataset()),
                                                                    framework::dataset::make("DataType", DataType::F32)),
                src_shape, weights_shape, bias_shape, dst_shape, info, data_type)