diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index d9109e4..37a8850 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -1141,7 +1141,7 @@
 public:
     /** Default constructor */
     GEMMInfo()
-        : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false)
+        : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false), _retain_internal_weights(false)
     {
     }
     /** Constructor
@@ -1152,11 +1152,12 @@
      * @param[in] depth_output_gemm3d         (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel
      * @param[in] reinterpret_input_as_3d     (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
      *                                        to perform 1x1 convolutions with the NHWC data layout)
+     * @param[in] retain_internal_weights     (Optional) Retain the weights tensor from previous run
      *
      */
-    GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false)
+    GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false)
         : _is_a_reshaped(is_a_reshaped), _is_b_reshaped(is_b_reshaped), _reshape_b_only_on_first_run(reshape_b_only_on_first_run), _depth_output_gemm3d(depth_output_gemm3d),
-          _reinterpret_input_as_3d(reinterpret_input_as_3d)
+          _reinterpret_input_as_3d(reinterpret_input_as_3d), _retain_internal_weights(retain_internal_weights)
     {
     }
     /** Flag which specifies if the matrix A has been reshaped
@@ -1201,6 +1202,14 @@
     {
         return _reinterpret_input_as_3d;
     };
+    /** Flag which specifies if the weights tensor has to be retained from previous run
+     *
+     * @return True if the weights tensor has to be retained
+     */
+    bool retain_internal_weights() const
+    {
+        return _retain_internal_weights;
+    };
 
 private:
     const bool _is_a_reshaped;
@@ -1208,6 +1217,7 @@
     const bool _reshape_b_only_on_first_run;
     const int  _depth_output_gemm3d;
     const bool _reinterpret_input_as_3d;
+    const bool _retain_internal_weights;
 };
 
 /** Winograd information */
diff --git a/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h b/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
index 450cd83..d6d88ce 100644
--- a/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
+++ b/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
@@ -125,9 +125,9 @@
     void prepare() override;
 
 private:
-    void configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);
-    void configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);
-    void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);
+    void configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights);
+    void configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights);
+    void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights);
 
     CLMemoryGroup                                       _memory_group;
     CLConvertFullyConnectedWeights                      _convert_weights;
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 60c28a0..010985d 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -78,7 +78,7 @@
       _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
 {
 }
-void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
 {
     if(_is_quantized)
     {
@@ -100,11 +100,11 @@
     else
     {
         // Configure matrix multiply kernel
-        _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
+        _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */, 1, false, retain_internal_weights));
     }
 }
 
-void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
 {
     ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
 
@@ -119,18 +119,18 @@
     _flatten_layer.configure(input, &_flatten_output);
 
     // Configure matrix multiply kernel
-    configure_mm(&_flatten_output, weights, output);
+    configure_mm(&_flatten_output, weights, output, retain_internal_weights);
 
     // Allocate the output tensor for flatten once all the configure methods have been called
     _flatten_output.allocator()->allocate();
 }
 
-void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
 {
     ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
 
     // Configure matrix multiply kernel
-    configure_mm(input, weights, output);
+    configure_mm(input, weights, output, retain_internal_weights);
 }
 
 void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
@@ -150,7 +150,7 @@
     _is_fc_after_conv      = true;
     _accumulate_biases     = false;
     _is_quantized          = is_data_type_quantized_asymmetric(input->info()->data_type());
-    _is_prepared           = false;
+    _is_prepared           = fc_info.retain_internal_weights;
     _original_weights      = weights;
 
     // Configure gemmlowp output
@@ -218,12 +218,12 @@
     if(_is_fc_after_conv)
     {
         // Fully Connected layer after a Convolution Layer without batches
-        configure_conv_fc(input, weights_to_use, tmp_output);
+        configure_conv_fc(input, weights_to_use, tmp_output, fc_info.retain_internal_weights);
     }
     else
     {
         // Fully Connected layer after a Fully Connected Layer without batches
-        configure_fc_fc(input, weights_to_use, tmp_output);
+        configure_fc_fc(input, weights_to_use, tmp_output, fc_info.retain_internal_weights);
     }
 
     // Configure output stage for asymmetric quantized types
@@ -235,8 +235,6 @@
         _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset);
         _gemmlowp_output.allocator()->allocate();
     }
-
-    _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
 }
 
 Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 1ad8531..85d90a0 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -86,7 +86,7 @@
 
     // Check if we need to reshape the matrix B only on the first run
     _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
-    _is_prepared                 = false;
+    _is_prepared                 = gemm_info.retain_internal_weights();
     _original_b                  = b;
 
     const ICLTensor *matrix_a = a;
diff --git a/tests/validation/CL/UNIT/WeightsRetention.cpp b/tests/validation/CL/UNIT/WeightsRetention.cpp
new file mode 100644
index 0000000..bfaef56
--- /dev/null
+++ b/tests/validation/CL/UNIT/WeightsRetention.cpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2018 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/CLFullyConnectedLayer.h"
+#include "support/ToolchainSupport.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/Globals.h"
+#include "tests/Utils.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/UNIT/WeightsRetentionFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.05f);
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(UNIT)
+TEST_SUITE(WeightsRetention)
+
+using CLWeightsRetentionFixture = WeightsRetentionReconfigureTestCaseFixture<CLTensor,
+      CLAccessor,
+      CLFullyConnectedLayer>;
+FIXTURE_TEST_CASE(WeightsRetention,
+                  CLWeightsRetentionFixture,
+                  framework::DatasetMode::ALL)
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/UNIT/WeightsRetentionFixture.h b/tests/validation/fixtures/UNIT/WeightsRetentionFixture.h
new file mode 100644
index 0000000..b17c003
--- /dev/null
+++ b/tests/validation/fixtures/UNIT/WeightsRetentionFixture.h
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2017-2018 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.
+ */
+#ifndef ARM_COMPUTE_TEST_UNIT_WEIGHTS_RETENTION
+#define ARM_COMPUTE_TEST_UNIT_WEIGHTS_RETENTION
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/FullyConnectedLayer.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+/** Test case to run a fully connected layer with weights retention, reconfigure
+ *  with different shapes and rerun making sure the weights are retained.
+ *
+ * Runs a fully connected layer stimulating is_interleaved_transpose CLGEMM,
+ * then reconfigures with different batch size and reruns.
+ */
+template <typename TensorType, typename AccessorType, typename FullyConnectedFunction>
+class WeightsRetentionReconfigureTestCaseFixture : public framework::Fixture
+{
+    using T = float;
+
+public:
+    void setup()
+    {
+        _max_batches = 8;
+        _cur_batches = 6;
+        _target      = compute_target();
+        _reference   = compute_reference();
+    };
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        std::uniform_real_distribution<> distribution(0.5f, 1.f);
+        library->fill(tensor, distribution, i);
+    }
+
+    TensorType compute_target()
+    {
+        // Create tensors
+        TensorType w1  = create_tensor<TensorType>(TensorShape(180000U, 150U), DataType::F32, 1);
+        TensorType b1  = create_tensor<TensorType>(TensorShape(150U), DataType::F32, 1);
+        TensorType src = create_tensor<TensorType>(TensorShape(1U, 150U, 1200U, _max_batches), DataType::F32, 1);
+        TensorType dst = create_tensor<TensorType>(TensorShape(150U, _max_batches), DataType::F32, 1);
+
+        // Create and configure function
+        FullyConnectedFunction fc_layer_1;
+        fc_layer_1.configure(&src, &w1, &b1, &dst);
+
+        // Allocate persistent tensors
+        w1.allocator()->allocate();
+        b1.allocator()->allocate();
+
+        // Allocate tensors (1st iteration)
+        src.allocator()->allocate();
+        dst.allocator()->allocate();
+
+        // Fill tensors (1st iteration)
+        fill(AccessorType(src), 0);
+        fill(AccessorType(w1), 1);
+        fill(AccessorType(b1), 2);
+
+        // Compute functions (1st iteration)
+        fc_layer_1.run();
+
+        // Update tensor shapes (2nd iteration)
+        auto src_padding     = src.allocator()->info().padding();
+        auto dst_padding     = dst.allocator()->info().padding();
+        int  diff            = _max_batches - _cur_batches;
+        auto new_src_padding = PaddingSize(src_padding.top, src_padding.right, src_padding.bottom + diff, src_padding.left);
+        auto new_dst_padding = PaddingSize(dst_padding.top, dst_padding.right, dst_padding.bottom + diff, dst_padding.left);
+        src.allocator()->info().set_tensor_shape(TensorShape(1U, 150U, 1200U, _cur_batches)).set_is_resizable(true).extend_padding(new_src_padding);
+        src.allocator()->info().set_is_resizable(false);
+        dst.allocator()->info().set_tensor_shape(TensorShape(150U, _cur_batches)).set_is_resizable(true).extend_padding(new_dst_padding);
+        dst.allocator()->info().set_is_resizable(false);
+
+        // Configure FC info
+        FullyConnectedLayerInfo fc_info;
+        fc_info.retain_internal_weights = true;
+
+        // Configure functions (2nd iteration)
+        fc_layer_1.configure(&src, &w1, &b1, &dst, fc_info);
+
+        // Fill tensors (2nd iteration)
+        fill(AccessorType(src), 5);
+
+        // Compute functions (2nd iteration)
+        fc_layer_1.run();
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference()
+    {
+        // Create reference
+        SimpleTensor<T> w1{ TensorShape(180000U, 150U), DataType::F32 };
+        SimpleTensor<T> b1{ TensorShape(150U), DataType::F32 };
+        SimpleTensor<T> src{ TensorShape(1U, 150U, 1200U, _cur_batches), DataType::F32 };
+
+        // Fill reference
+        fill(src, 5);
+        fill(w1, 1);
+        fill(b1, 2);
+
+        return reference::fully_connected_layer(src, w1, b1, TensorShape(150U, _cur_batches));
+    }
+
+protected:
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+    unsigned int    _max_batches{};
+    unsigned int    _cur_batches{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_UNIT_WEIGHTS_RETENTION */