diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
new file mode 100644
index 0000000..7408054
--- /dev/null
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMMatrixAdditionKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMTranspose1xWKernel.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/ITensorAllocator.h"
+
+using namespace arm_compute;
+
+CLGEMM::CLGEMM()
+    : _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _run_vector_matrix_multiplication(false), _run_addition(false)
+{
+}
+
+void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::F16);
+
+    if(c != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(c, 1, DataType::F32, DataType::F16);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c);
+        ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
+        ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
+        ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix");
+        ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+    ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
+
+    // Check if the first input tensor is a vector. If so, all the kernels for reshaping the tensors can be skipped
+    if(a->info()->dimension(1) != 1)
+    {
+        _run_vector_matrix_multiplication = false;
+
+        TensorShape shape_tmp_a = a->info()->tensor_shape();
+        TensorShape shape_tmp_b = b->info()->tensor_shape();
+
+        shape_tmp_a.set(0, a->info()->dimension(0) * 4);
+        shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
+
+        if(DataType::F32 == a->info()->data_type())
+        {
+            shape_tmp_b.set(0, b->info()->dimension(1) * 4);
+            shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 4.0f));
+        }
+        else if(DataType::F16 == a->info()->data_type())
+        {
+            shape_tmp_b.set(0, b->info()->dimension(1) * 8);
+            shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 8.0f));
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR("DataType not supported");
+        }
+
+        TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type());
+        _tmp_a.allocator()->init(info_a);
+
+        TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type());
+        _tmp_b.allocator()->init(info_b);
+
+        // Configure interleave kernel
+        _interleave_kernel.configure(a, &_tmp_a);
+
+        // Configure transpose kernel
+        _transpose_kernel.configure(b, &_tmp_b);
+
+        // Configure matrix multiply kernel
+        _mm_kernel.configure(&_tmp_a, &_tmp_b, output, alpha);
+
+        // Allocate intermediate tensors
+        _tmp_a.allocator()->allocate();
+        _tmp_b.allocator()->allocate();
+    }
+    else // The first input tensor is a vector
+    {
+        _run_vector_matrix_multiplication = true;
+
+        // Configure the matrix multiply kernel
+        _mm_kernel.configure(a, b, output, alpha);
+    }
+
+    // Configure matrix addition kernel
+    if(beta != 0 && c != nullptr)
+    {
+        _ma_kernel.configure(c, output, beta);
+        _run_addition = true;
+    }
+}
+
+void CLGEMM::run()
+{
+    if(!_run_vector_matrix_multiplication)
+    {
+        // Run interleave kernel
+        CLScheduler::get().enqueue(_interleave_kernel, false);
+
+        // Run transpose kernel
+        CLScheduler::get().enqueue(_transpose_kernel, false);
+    }
+
+    // Run matrix multiply kernel
+    CLScheduler::get().enqueue(_mm_kernel, !_run_addition);
+
+    // Run matrix addition kernel
+    if(_run_addition)
+    {
+        CLScheduler::get().enqueue(_ma_kernel);
+    }
+}