Add Bias to MatMul Kernels and add support for use in Fully Connected Layer

Resolves: [COMPMID-6316]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I08e6bac9e6b46b76978da0dc6a48ccfe3dde5086
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9833
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 279 insertions, 119 deletions

diff --git a/tests/validation/CL/MatMulKernel.cpp b/tests/validation/CL/MatMulKernel.cpp
index ff872aaa0a..b47f8bc924 100644
--- a/tests/validation/CL/MatMulKernel.cpp
+++ b/tests/validation/CL/MatMulKernel.cpp
@@ -75,6 +75,9 @@ const auto k0_values_nightly_lhs_t_rhs_nt  = framework::dataset::make("K0", { 1,
 template <typename T>
 using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>;
 
+template <typename T>
+using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeKernel>;
+
 TEST_SUITE(CL)
 TEST_SUITE(MatMulKernel)
 TEST_SUITE(Validate)
@@ -162,7 +165,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
     for(auto &pair : supported_block_sizes)
     {
         TensorInfo output_info;
-        Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+        Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
 
         if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported)
         {
@@ -222,7 +225,7 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
             };
 
             TensorInfo output_info;
-            Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+            Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);
 
             const bool expected = std::get<4>(tuple);
             ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
@@ -233,22 +236,25 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
 TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 {
     // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
-    using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+    using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>;
     const std::vector<ShapeConfigurationTuple> shape_configurations =
     {
-        { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
-        { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
-        { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
-        { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
-        { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
-        { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
-        { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
-        { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
+        { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
+        { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+        { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
+        { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+        { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+        { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
+        { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false },                                 // Unsupported bias broadcasting.
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false },                             // 2D bias is unsupported.
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(6U), false },                                 // bias first dimension != dst first dimension
     };
 
     for(auto &tuple : shape_configurations)
     {
-        const bool expected = std::get<2>(tuple);
+        const bool expected = std::get<3>(tuple);
 
         for(bool adj_lhs :
             {
@@ -262,6 +268,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
             {
                 TensorShape lhs_shape = std::get<0>(tuple);
                 TensorShape rhs_shape = std::get<1>(tuple);
+                TensorShape bia_shape = std::get<2>(tuple);
 
                 if(adj_lhs)
                 {
@@ -275,11 +282,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 
                 const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
                 const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+                const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
                 TensorInfo       output_info;
 
                 MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
 
-                Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+                Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
                 ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
             }
         }
@@ -322,7 +330,7 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
         const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
         TensorInfo       output_info(shape, 1, std::get<2>(tuple));
 
-        Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+        Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);
         ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
     }
 }
@@ -356,6 +364,19 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::Datase
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
 }
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+                                                                                                                   framework::dataset::make("TransposeA", { false, true })),
+                                                                                                                   framework::dataset::make("TransposeB", { false, true })),
+                                                                                                                   m0_values_precommit),
+                                                                                                                   n0_values_precommit),
+                                                                                                                   k0_values_precommit),
+                                                                                                                   framework::dataset::make("ExportRhsToCLImage", { false })),
+                                                                                                           framework::dataset::make("DataType", DataType::F32)))
+
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
 FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                    framework::dataset::make("TransposeA", { false })),
                                                                                                                    framework::dataset::make("TransposeB", { false })),
diff --git a/tests/validation/CL/MatMulLowpNativeKernel.cpp b/tests/validation/CL/MatMulLowpNativeKernel.cpp
index fd7a4cb156..90eee4fb82 100644
--- a/tests/validation/CL/MatMulLowpNativeKernel.cpp
+++ b/tests/validation/CL/MatMulLowpNativeKernel.cpp
@@ -49,6 +49,9 @@ constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for
 template <typename T>
 using CLMatMulLowpNativeKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeKernel>;
 
+template <typename T>
+using CLMatMulLowpKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeKernel>;
+
 /** M0 values to test --precommit*/
 const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
 
@@ -103,7 +106,7 @@ TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL)
     for(auto &pair : supported_block_sizes)
     {
         TensorInfo output_info;
-        Status     status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+        Status     status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
 
         ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
     }
@@ -112,22 +115,24 @@ TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL)
 TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 {
     // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
-    using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+    using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>;
     const std::vector<ShapeConfigurationTuple> shape_configurations =
     {
-        { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
-        { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
-        { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
-        { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
-        { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
-        { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
-        { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
-        { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
+        { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
+        { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+        { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
+        { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+        { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+        { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
+        { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false },                                 // invalid broadcast of bias
+        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false },                             // 2d bias is invalid
     };
 
     for(auto &tuple : shape_configurations)
     {
-        const bool expected = std::get<2>(tuple);
+        const bool expected = std::get<3>(tuple);
 
         for(bool adj_lhs :
             {
@@ -141,6 +146,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
             {
                 TensorShape lhs_shape = std::get<0>(tuple);
                 TensorShape rhs_shape = std::get<1>(tuple);
+                TensorShape bia_shape = std::get<2>(tuple);
 
                 if(adj_lhs)
                 {
@@ -154,11 +160,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 
                 const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::QASYMM8_SIGNED);
                 const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED);
+                const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::S32);
                 TensorInfo       output_info;
 
                 MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
 
-                Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+                Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
                 ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
             }
         }
@@ -167,41 +174,44 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 
 TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
 {
-    using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+    using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
     const std::vector<DataTypeConfigurationTuple> data_type_configurations =
     {
-        { DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
-        { DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
-        { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
-        { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, true },
-        { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, true },
-        { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
-        { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false },                               // only qasymm8/qasymm8_signed is supported
-        { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false },                                  // only qasymm8/qasymm8_signed is supported
-        { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false },                                     // only qasymm8/qasymm8_signed is supported
-        { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM8, false },                           // no mixed data types
-        { DataType::S64, DataType::S64, DataType::S64, false },                                              // no integral types
-        { DataType::S32, DataType::S32, DataType::S32, false },                                              // no integral types
-        { DataType::S16, DataType::S16, DataType::S16, false },                                              // no integral types
-        { DataType::S8, DataType::S8, DataType::S8, false },                                                 // no integral types
-        { DataType::U64, DataType::U64, DataType::U64, false },                                              // no integral types
-        { DataType::U32, DataType::U32, DataType::U32, false },                                              // no integral types
-        { DataType::U16, DataType::U16, DataType::U16, false },                                              // no integral types
-        { DataType::U8, DataType::U8, DataType::U8, false },                                                 // no integral types
+        { DataType::F32, DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
+        { DataType::F16, DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
+        { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+        { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, true },
+        { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, true },
+        { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
+        { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false },                               // only qasymm8/qasymm8_signed is supported
+        { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false },                                  // only qasymm8/qasymm8_signed is supported
+        { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false },                                     // only qasymm8/qasymm8_signed is supported
+        { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8, false },                           // no mixed data types
+        { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false },                                              // no integral types
+        { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false },                                              // no integral types
+        { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false },                                              // no integral types
+        { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false },                                                  // no integral types
+        { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false },                                              // no integral types
+        { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false },                                              // no integral types
+        { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false },                                              // no integral types
+        { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false },                                                  // no integral types
+        { DataType::QASYMM8, DataType::QASYMM8, DataType::F32, DataType::QASYMM8, false }                                   // Only S32 bias is supported
     };
 
     // It's enough to test a single shape and block size configuration while checking data types
-    const TensorShape      shape = TensorShape(10U, 10U);
+    const TensorShape      shape     = TensorShape(10U, 10U);
+    const TensorShape      bia_shape = TensorShape(10U);
     const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
     for(auto &tuple : data_type_configurations)
     {
-        const bool expected = std::get<3>(tuple);
+        const bool expected = std::get<4>(tuple);
 
         const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
         const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
-        TensorInfo       output_info(shape, 1, std::get<2>(tuple));
+        const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
+        TensorInfo       output_info(shape, 1, std::get<3>(tuple));
 
-        Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+        Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
         ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
     }
 }
@@ -234,6 +244,18 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<int8_t>, framew
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_quant);
 }
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulLowpKernelWithBiasFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+                                                                                                                    framework::dataset::make("TransposeA", { true, false })),
+                                                                                                                    framework::dataset::make("TransposeB", { true, false })),
+                                                                                                                    m0_values_precommit),
+                                                                                                                    n0_values_precommit),
+                                                                                                                    k0_values_precommit),
+                                                                                                                    framework::dataset::make("ExportRhsToCLImage", { false })),
+                                                                                                                    framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_quant);
+}
 FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                framework::dataset::make("TransposeA", { false })),
diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp
index b63af75169..70c80985db 100644
--- a/tests/validation/CL/MatMulNativeMMULKernel.cpp
+++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp
@@ -70,6 +70,9 @@ const auto k0_value = framework::dataset::make("K0", { 1 });
 template <typename T>
 using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
 
+template <typename T>
+using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+
 TEST_SUITE(CL)
 TEST_SUITE(MatMulNativeMMULKernel)
 TEST_SUITE(Validate)
@@ -117,7 +120,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
             { MatMulKernelInfo(true, true, 3, 7, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
             { MatMulKernelInfo(true, true, 6, 3, 1), false },  // M0 not in {1, 2, 3, 4, 8, 16}
             { MatMulKernelInfo(true, true, 5, 3, 1), false },  // M0 not in {1, 2, 3, 4, 8, 16}
-            { MatMulKernelInfo(true, true, 4, 8, 2), false },   // K0 is not 1
+            { MatMulKernelInfo(true, true, 4, 8, 2), false },  // K0 is not 1
             { MatMulKernelInfo(true, true, 4, 8, 1), true },
             { MatMulKernelInfo(true, true, 3, 3, 1), true },
             { MatMulKernelInfo(true, true, 16, 4, 1), true },
@@ -132,7 +135,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
         for(auto &pair : supported_block_sizes)
         {
             TensorInfo output_info;
-            Status     status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+            Status     status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
             ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
         }
     }
@@ -148,28 +151,30 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
     if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
     {
         // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
-        using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+        using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; // lhs, rhs, bias, result
         const std::vector<ShapeConfigurationTuple> shape_configurations =
         {
-            { TensorShape(4U, 1U), TensorShape(3U, 4U), true },
-            { TensorShape(12U, 12U), TensorShape(3U, 12U), true },
-            { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
-            { TensorShape(8U, 4U), TensorShape(2U, 4U), false }, // Mismatch in the K dimension
-            { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
-            { TensorShape(5U, 7U), TensorShape(2U, 5U), false }, // K not a multiple of 4 (MMUL_K0)
-            { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), true },
-            { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // No batch broadcasting
-            { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // Mismatch in batch dimension
+            { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(3U), true },
+            { TensorShape(12U, 12U), TensorShape(3U, 12U), TensorShape(3U), true },
+            { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+            { TensorShape(8U, 4U), TensorShape(2U, 4U), TensorShape(2U), false }, // Mismatch in the K dimension
+            { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+            { TensorShape(5U, 7U), TensorShape(2U, 5U), TensorShape(2U), false }, // K not a multiple of 4 (MMUL_K0)
+            { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+            { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // No batch broadcasting
+            { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // Mismatch in batch dimension
+            { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(1U), false },                                 // Bias first dimensions != dst first dimension.
+            { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(5U, 6U), false },                             // Bias is 2d which is invalid.
         };
 
         for(auto &tuple : shape_configurations)
         {
-            const bool expected = std::get<2>(tuple);
+            const bool expected = std::get<3>(tuple);
 
-        for(bool adj_lhs :
-            {
-                false, true
-            })
+            for(bool adj_lhs :
+                {
+                    false, true
+                })
             {
                 for(bool adj_rhs :
                     {
@@ -178,6 +183,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
                 {
                     TensorShape lhs_shape = std::get<0>(tuple);
                     TensorShape rhs_shape = std::get<1>(tuple);
+                    TensorShape bia_shape = std::get<2>(tuple);
 
                     if(adj_lhs)
                     {
@@ -191,11 +197,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
 
                     const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
                     const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+                    const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
                     TensorInfo       output_info;
 
                     MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
 
-                    Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+                    Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
                     ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
                 }
             }
@@ -213,40 +220,44 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
     if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
     {
         // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
-        using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+        using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
         const std::vector<DataTypeConfigurationTuple> data_type_configurations =
         {
-            { DataType::F32, DataType::F32, DataType::F32, true },
-            { DataType::F16, DataType::F16, DataType::F16, true },
-            { DataType::F16, DataType::F32, DataType::F32, false },                                              // no mixed precision
-            { DataType::F64, DataType::F64, DataType::F64, false },                                              // no double precision
-            { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false },                                  // no quantized types
-            { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false },             // no quantized types
-            { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
-            { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false },                               // no quantized types
-            { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false },                                  // no quantized types
-            { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false },                                     // no quantized types
-            { DataType::S64, DataType::S64, DataType::S64, false },                                              // no integral types
-            { DataType::S32, DataType::S32, DataType::S32, false },                                              // no integral types
-            { DataType::S16, DataType::S16, DataType::S16, false },                                              // no integral types
-            { DataType::S8, DataType::S8, DataType::S8, false },                                                 // no integral types
-            { DataType::U64, DataType::U64, DataType::U64, false },                                              // no integral types
-            { DataType::U32, DataType::U32, DataType::U32, false },                                              // no integral types
-            { DataType::U16, DataType::U16, DataType::U16, false },                                              // no integral types
-            { DataType::U8, DataType::U8, DataType::U8, false },                                                 // no integral types
+            { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+            { DataType::F16, DataType::F16, DataType::F16, DataType::F16, true },
+            { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+            { DataType::F32, DataType::F32, DataType::F16, DataType::F32, false },                                              // incorrect bias type
+            { DataType::F16, DataType::F32, DataType::F32, DataType::F32, false },                                              // no mixed precision
+            { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false },                                              // no double precision
+            { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, false },                                  // no quantized types
+            { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, false },             // no quantized types
+            { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
+            { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false },                               // no quantized types
+            { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false },                                  // no quantized types
+            { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false },                                     // no quantized types
+            { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false },                                              // no integral types
+            { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false },                                              // no integral types
+            { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false },                                              // no integral types
+            { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false },                                                  // no integral types
+            { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false },                                              // no integral types
+            { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false },                                              // no integral types
+            { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false },                                              // no integral types
+            { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false },                                                  // no integral types
         };
 
-        const TensorShape      shape = TensorShape(8U, 8U);
+        const TensorShape      shape     = TensorShape(8U, 8U);
+        const TensorShape      bia_shape = TensorShape(8U);
         const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
         for(auto &tuple : data_type_configurations)
         {
-            const bool expected = std::get<3>(tuple);
+            const bool expected = std::get<4>(tuple);
 
             const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
             const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
-            TensorInfo       output_info(shape, 1, std::get<2>(tuple));
+            const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
+            TensorInfo       output_info(shape, 1, std::get<3>(tuple));
 
-            Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+            Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
             ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
         }
     }
@@ -292,7 +303,23 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<float>, framewo
         validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
     }
 }
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(),
+                                                                                                                   framework::dataset::make("TransposeA", { false, true })),
+                                                                                                                   framework::dataset::make("TransposeB", { false, true })),
+                                                                                                                   m0_values_precommit),
+                                                                                                                   n0_values_precommit),
+                                                                                                                   k0_value),
+                                                                                                                   framework::dataset::make("ExportRhsToCLImage", { false })),
+                                                                                                           framework::dataset::make("DataType", DataType::F32)))
+{
+    // Validate output
+    if(_device_supports_mmul)
+    {
+        validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+    }
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
                                                                                framework::dataset::make("TransposeA", { false })),
                                                                        framework::dataset::make("TransposeB", { false })),
                                                                m0_values_nightly_lhs_nt),
@@ -308,7 +335,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<floa
     }
 }
 
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
                                                                                framework::dataset::make("TransposeA", { false })),
                                                                        framework::dataset::make("TransposeB", { true })),
                                                                m0_values_nightly_lhs_nt),
@@ -323,14 +351,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<flo
         validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
     }
 }
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
-                                                                                                                    framework::dataset::make("TransposeA", { true })),
-                                                                                                                    framework::dataset::make("TransposeB", { false })),
-                                                                                                                    m0_values_nightly_lhs_t),
-                                                                                                                    n0_values_nightly_rhs_nt),
-                                                                                                                    k0_value),
-                                                                                                                    framework::dataset::make("ExportRhsToCLImage", { false })),
-                                                                                                                    framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+                                                                               framework::dataset::make("TransposeA", { true })),
+                                                                       framework::dataset::make("TransposeB", { false })),
+                                                               m0_values_nightly_lhs_t),
+                                                       n0_values_nightly_rhs_nt),
+                                               k0_value),
+                                       framework::dataset::make("ExportRhsToCLImage", { false })),
+                               framework::dataset::make("DataType", DataType::F32)))
 {
     // Validate output
     // Validate output
@@ -395,7 +424,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<half>, framewor
         validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
     }
 }
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
                                                                                framework::dataset::make("TransposeA", { false })),
                                                                        framework::dataset::make("TransposeB", { false })),
                                                                m0_values_nightly_lhs_nt),
@@ -410,7 +440,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half
         validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
     }
 }
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
                                                                                framework::dataset::make("TransposeA", { false })),
                                                                        framework::dataset::make("TransposeB", { true })),
                                                                m0_values_nightly_lhs_nt),
@@ -425,14 +456,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<hal
         validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
     }
 }
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
-                                                                                                                    framework::dataset::make("TransposeA", { true })),
-                                                                                                                    framework::dataset::make("TransposeB", { false })),
-                                                                                                                    m0_values_nightly_lhs_t),
-                                                                                                                    n0_values_nightly_rhs_nt),
-                                                                                                                    k0_value),
-                                                                                                                    framework::dataset::make("ExportRhsToCLImage", { false })),
-                                                                                                                    framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+                                                                               framework::dataset::make("TransposeA", { true })),
+                                                                       framework::dataset::make("TransposeB", { false })),
+                                                               m0_values_nightly_lhs_t),
+                                                       n0_values_nightly_rhs_nt),
+                                               k0_value),
+                                       framework::dataset::make("ExportRhsToCLImage", { false })),
+                               framework::dataset::make("DataType", DataType::F16)))
 {
     // Validate output
     // Validate output
diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h
index 59bcfe5b2d..88fdf8b291 100644
--- a/tests/validation/fixtures/MatMulKernelFixture.h
+++ b/tests/validation/fixtures/MatMulKernelFixture.h
@@ -36,7 +36,7 @@
 #include "tests/validation/reference/GEMMLowp.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/ReshapeLayer.h"
-
+#include <cmath>
 #include <random>
 
 namespace arm_compute
@@ -48,12 +48,16 @@ namespace validation
 using namespace arm_compute::opencl::kernels;
 
 template <typename T, typename KernelType, bool use_mmul = false>
-class MatMulKernelValidationFixture : public framework::Fixture
+class MatMulKernelGenericValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+    void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type,
+               bool enable_bias)
     {
+        // Flag to create a bias
+        _enable_bias = enable_bias;
+
         // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
         QuantizationInfo lhs_q_info;
         QuantizationInfo rhs_q_info;
@@ -138,6 +142,16 @@ protected:
         }
     }
 
+    template <typename U>
+    void fill_bias_s32(U &&tensor, int i, const UniformQuantizationInfo &q_info)
+    {
+        // For quantized cases, fill the S32 bias according to the following to avoid saturation of test cases.
+        // The following code limits size of bias values to within expected range of output quantization.
+        const unsigned int                     bound = std::abs(q_info.scale * 256); // 256 is size of 8 bit datatype
+        std::uniform_int_distribution<int32_t> distribution(-(bound / 10), (bound / 10));
+        library->fill(tensor, distribution, i);
+    }
+
     template <typename U, typename D>
     void fill_constant(U &&tensor, D value)
     {
@@ -156,12 +170,15 @@ protected:
         matmul_info.k0                     = K0;
         matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;
 
+        bool is_quantized = is_data_type_quantized(data_type);
+
         // Create tensors
-        CLTensor a   = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
-        CLTensor b   = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
-        CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);
+        CLTensor a    = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
+        CLTensor b    = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
+        CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info);
+        CLTensor dst  = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);
 
-        matMul.configure(a.info(), b.info(), dst.info(), matmul_info);
+        matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, dst.info(), matmul_info);
         ARM_COMPUTE_ASSERT(a.info()->is_resizable());
         ARM_COMPUTE_ASSERT(b.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
@@ -184,6 +201,22 @@ protected:
             { ACL_SRC_1, &b },
             { ACL_DST, &dst }
         });
+
+        if(_enable_bias)
+        {
+            // Allocate, fill and add bias to TensorPack obj
+            bias.allocator()->allocate();
+            if(is_quantized)
+            {
+                fill_bias_s32(CLAccessor(bias), 2, dst_q_info.uniform());
+            }
+            else
+            {
+                fill(CLAccessor(bias), 2);
+            }
+            tensors_pack.add_tensor(ACL_SRC_2, &bias);
+        }
+
         matMul.run(tensors_pack);
 
         return dst;
@@ -252,9 +285,21 @@ protected:
     template <typename U = T>
     typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
     {
+        // Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast
+        // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel
+        if(_enable_bias)
+        {
+            fill(c, 2);
+            const int n          = c.shape().x();
+            const int other_dims = c.shape().collapsed_from(1)[1];
+            for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches
+            {
+                memcpy(c.data() + i * n, c.data(), n * sizeof(T));
+            }
+        }
         // Setting beta to 0 will effectively disable C for the
         // computation of the reference: alpha * A * B + 0 * C
-        return reference::gemm<U>(a, b, c, 1.0f, 0.f);
+        return reference::gemm<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f);
     }
 
     template <typename U = T>
@@ -276,19 +321,59 @@ protected:
         constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();
 
         SimpleTensor<int> bias{ c.shape(), DataType::S32 };
-        fill_constant(bias, static_cast<int32_t>(0));
+        if(_enable_bias)
+        {
+            // Identical to float implementation, fill and copy values of bias first dimension
+            fill_bias_s32(bias, 2, cq);
+            const int          n          = bias.shape().x();
+            const int          other_dims = bias.shape().collapsed_from(1)[1];
+            const unsigned int dt_size    = sizeof(int32_t);
+            for(int i = 1; i < other_dims; ++i)
+            {
+                memcpy(bias.data() + i * n, bias.data(), n * dt_size);
+            }
+        }
+        else
+        {
+            fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias
+        }
 
         const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
                                                                                                                gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);
+
         return final_result;
     }
 
     CLTensor        _target{};
     SimpleTensor<T> _reference{};
+    bool            _enable_bias{ false };
     bool            _device_supports_export_to_cl_image{ true };
     bool            _device_supports_mmul{ true };
 };
 
+template <typename T, typename KernelType, bool use_mmul = false>
+class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+    {
+        MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
+                                                                             false /* enable bias */);
+    }
+};
+
+template <typename T, typename KernelType, bool use_mmul = false>
+class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+    {
+        MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
+                                                                             true /* enable bias */);
+    }
+};
 } // namespace validation
 } // namespace test
 } // namespace arm_compute