diff --git a/include/armnn/IRuntime.hpp b/include/armnn/IRuntime.hpp
index d85a3e3..042271f 100644
--- a/include/armnn/IRuntime.hpp
+++ b/include/armnn/IRuntime.hpp
@@ -216,18 +216,19 @@
     TensorInfo GetInputTensorInfo(NetworkId networkId, LayerBindingId layerId) const;
     TensorInfo GetOutputTensorInfo(NetworkId networkId, LayerBindingId layerId) const;
 
-
     /// ImportInputs separates the importing and mapping of InputTensors from network execution.
     /// Allowing for a set of InputTensors to be imported and mapped once, but used in execution many times.
     /// This function is not thread safe and must not be used while other threads are calling Execute().
-    /// Only compatible with AsyncEnabled networks
-    std::vector<ImportedInputId> ImportInputs(NetworkId networkId, const InputTensors& inputTensors);
+    /// Only compatible with AsyncEnabled networks and aligned memory import
+    std::vector<ImportedInputId> ImportInputs(NetworkId networkId, const InputTensors& inputTensors,
+                                              MemorySource forceImportMemorySource = MemorySource::Undefined);
 
     /// ImportOutputs separates the importing and mapping of OutputTensors from network execution.
     /// Allowing for a set of OutputTensors to be imported and mapped once, but used in execution many times.
     /// This function is not thread safe and must not be used while other threads are calling Execute().
-    /// Only compatible with AsyncEnabled networks
-    std::vector<ImportedOutputId> ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors);
+    /// Only compatible with AsyncEnabled networks and aligned memory import
+    std::vector<ImportedOutputId> ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors,
+                                                MemorySource forceImportMemorySource = MemorySource::Undefined);
 
     /// Un-import and delete the imported InputTensor/s
     /// This function is not thread safe and must not be used while other threads are calling Execute().
@@ -242,7 +243,9 @@
     /// Evaluates a network using input in inputTensors and outputs filled into outputTensors
     Status EnqueueWorkload(NetworkId networkId,
                            const InputTensors& inputTensors,
-                           const OutputTensors& outputTensors);
+                           const OutputTensors& outputTensors,
+                           std::vector<ImportedInputId> preImportedInputIds = {},
+                           std::vector<ImportedOutputId> preImportedOutputIds = {});
 
     /// This is an experimental function.
     /// Evaluates a network using input in inputTensors and outputs filled into outputTensors.
diff --git a/src/armnn/LoadedNetwork.cpp b/src/armnn/LoadedNetwork.cpp
index 1d1aae5..45891f7 100644
--- a/src/armnn/LoadedNetwork.cpp
+++ b/src/armnn/LoadedNetwork.cpp
@@ -314,21 +314,22 @@
                     {
                         if (layer->GetNumInputSlots() >= 1)
                         {
-                            unsigned int slotIndex = 0;
+                            unsigned int inputSlotIndex = 0;
                             for (auto& inputSlot : layer->GetInputSlots())
                             {
                                 if (inputSlot.GetOwningLayer().GetType() == LayerType::Input)
                                 {
-                                    m_InputWorkloadSlotPairs.push_back(
-                                        std::make_pair(m_WorkloadQueue.size(), slotIndex));
+                                    auto inputLayer = PolymorphicDowncast<InputLayer*>(&inputSlot.GetOwningLayer());
+                                    m_InputWorkloadSlotPairs[inputLayer->GetBindingId()] =
+                                        std::make_pair(m_WorkloadQueue.size(), inputSlotIndex);
                                 }
-                                ++slotIndex;
+                                ++inputSlotIndex;
                             }
                         }
 
                         if (layer->GetNumOutputSlots() >= 1)
                         {
-                            unsigned int slotIndex = 0;
+                            unsigned int outputSlotIndex = 0;
                             for (auto& outputSlot : layer->GetOutputSlots())
                             {
                                 for (unsigned int i = 0; i < outputSlot.GetNumConnections(); i++)
@@ -337,12 +338,14 @@
                                     // Add its index within layer->GetOutputSlots() to m_OutputWorkloadSlotPairs
                                     if (outputSlot.GetConnection(i)->GetOwningLayer().GetType() == LayerType::Output)
                                     {
-                                        m_OutputWorkloadSlotPairs.push_back(
-                                            std::make_pair(m_WorkloadQueue.size(), slotIndex));
+                                        auto outputLayer = PolymorphicDowncast<OutputLayer*>(
+                                            &outputSlot.GetConnection(i)->GetOwningLayer());
+                                        m_OutputWorkloadSlotPairs[outputLayer->GetBindingId()] =
+                                            std::make_pair(m_WorkloadQueue.size(), outputSlotIndex);
                                         continue;
                                     }
                                 }
-                                ++slotIndex;
+                                ++outputSlotIndex;
                             }
                         }
                         m_WorkloadQueue.push_back(std::move(workload));
@@ -667,7 +670,9 @@
 }
 
 Status LoadedNetwork::EnqueueWorkload(const InputTensors& inputTensors,
-                                      const OutputTensors& outputTensors)
+                                      const OutputTensors& outputTensors,
+                                      std::vector<ImportedInputId> preImportedInputIds,
+                                      std::vector<ImportedOutputId> preImportedOutputIds)
 {
     const Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();
 
@@ -691,10 +696,26 @@
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
         m_InputQueue.clear();
         m_InputQueue.reserve(graph.GetNumInputs());
+
         for (const BindableLayer* inputLayer : graph.GetInputLayers())
         {
-            const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());
-            EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
+            if (preImportedInputIds.size() != m_PreImportedInputHandles.size())
+            {
+                throw InvalidArgumentException("Invalid number of preImportedInputIds");
+            }
+            auto layerBindingId = inputLayer->GetBindingId();
+            auto it = std::find_if(preImportedInputIds.begin(), preImportedInputIds.end(),
+                                   [=](auto preImportedInputId)
+            {
+                return m_PreImportedInputHandles[preImportedInputId].m_LayerBindingId == layerBindingId;
+            });
+
+            if (it == preImportedInputIds.end())
+            {
+                // InputTensorHandle is not imported yet, process to enqueue input
+                const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());
+                EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
+            }
         }
     }
 
@@ -703,12 +724,57 @@
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
         m_OutputQueue.clear();
         m_OutputQueue.reserve(graph.GetNumOutputs());
+
         for (const BindableLayer* outputLayer : graph.GetOutputLayers())
         {
+            if (preImportedOutputIds.size() != m_PreImportedOutputHandles.size())
+            {
+                throw InvalidArgumentException("Invalid number of preImportedOutputIds");
+            }
+            auto layerBindingId = outputLayer->GetBindingId();
+            auto it = std::find_if(preImportedOutputIds.begin(), preImportedOutputIds.end(),
+                                   [=](auto preImportedOutputId)
+            {
+                return m_PreImportedOutputHandles[preImportedOutputId].m_LayerBindingId == layerBindingId;
+            });
+
             const TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId());
-            EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
+
+            if (it == preImportedOutputIds.end())
+            {
+                // OutputTensorHandle is not imported yet, process to enqueue Output
+                EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
+            }
+            else
+            {
+                // Insert synchronization workload for the imported output
+                OutputQueueDescriptor outputQueueDescriptor;
+                WorkloadInfo info;
+
+                outputQueueDescriptor.m_Outputs.push_back(pin.GetTensorHandle());
+                info.m_OutputTensorInfos.push_back(pin.GetTensorInfo());
+
+                // Gets the output handler from the previous node.
+                const OutputHandler& outputHandler =
+                    outputLayer->GetInputSlots()[0].GetConnectedOutputSlot()->GetOutputHandler();
+
+                const TensorInfo& inputTensorInfo = outputHandler.GetTensorInfo();
+                ITensorHandle* inputTensorHandle = outputHandler.GetData();
+                ARMNN_ASSERT_MSG(inputTensorHandle != nullptr, "Data should have been allocated.");
+                MemSyncQueueDescriptor syncDesc;
+                syncDesc.m_Inputs.push_back(inputTensorHandle);
+                info.m_InputTensorInfos.push_back(inputTensorInfo);
+                auto syncWorkload = std::make_unique<SyncMemGenericWorkload>(syncDesc, info);
+                ARMNN_ASSERT_MSG(syncWorkload, "No sync workload created");
+                m_OutputQueue.push_back(move(syncWorkload));
+            }
         }
     }
+    // Clear m_PreImportedInputHandles and m_PreImportedOutputHandles
+    m_PreImportedInputHandles.clear();
+    m_PreImportedOutputHandles.clear();
+    m_CurImportedInputId = 0;
+    m_CurImportedOutputId = 0;
 
     std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                         TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
@@ -1120,90 +1186,260 @@
     throw InvalidArgumentException("Output does not exist.");
 }
 
-std::vector<ImportedInputId> LoadedNetwork::ImportInputs(const InputTensors& inputTensors)
+std::vector<ImportedInputId> LoadedNetwork::ImportInputs(const InputTensors& inputTensors,
+                                                         MemorySource forceImportMemorySource)
 {
-    if (!m_NetworkProperties.m_ImportEnabled)  // Try import the input tensor
+    if (!m_NetworkProperties.m_ImportEnabled)
     {
-        throw MemoryImportException("ImportInputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
+        // Cannot import if import is not enabled and forceImportMemorySource is undefined
+        if (forceImportMemorySource == MemorySource::Undefined)
+        {
+            throw MemoryImportException("ImportInputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
+        }
+        // If forceImportMemorySource is defined, try import if memory is aligned
+        if (inputTensors.size() != m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumInputs())
+        {
+            throw MemoryImportException("ImportInputs: Force Import failed, incorrect number of tensors");
+        }
+
+        std::vector<ImportedInputId> importedInputs;
+        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
+        for (auto inputTensor : inputTensors)
+        {
+            auto layerBindingId = inputTensor.first;
+            auto it = std::find_if(graph.GetInputLayers().begin(), graph.GetInputLayers().end(), [=](auto* layer)
+            {
+                return layer->GetBindingId() == layerBindingId;
+            });
+
+            if (it == graph.GetInputLayers().end())
+            {
+                throw MemoryImportException(fmt::format(
+                    "ImportInputs: Memory Import failed, unknown LayerBindingId: {}", layerBindingId));
+            }
+
+            const Layer* layer = *it;
+            if (layer->GetType() != LayerType::Input)
+            {
+                throw InvalidArgumentException("ImportInputs: given layer not an InputLayer");
+            }
+            const OutputSlot& outputSlot = layer->GetOutputSlots()[0];
+            ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
+            // Get matching import factory Id
+            ITensorHandleFactory::FactoryId importFactoryId =
+                m_TensorHandleFactoryRegistry.GetMatchingImportFactoryId(factoryId);
+            ITensorHandleFactory* importFactory =
+                m_TensorHandleFactoryRegistry.GetFactory(importFactoryId, forceImportMemorySource);
+            if (!importFactory)
+            {
+                throw MemoryImportException("ImportInputs: Force Import failed, cannot find matching Import Factory");
+            }
+
+            OutputHandler& handler = const_cast<OutputHandler&>(layer->GetOutputHandler(0));
+            handler.SetAllocatedData();
+            handler.CreateTensorHandles(*importFactory, false);
+            ITensorHandle* outputTensorHandle = handler.GetData();
+            std::unique_ptr<ITensorHandle> passThroughTensorHandle =
+                    std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
+                                                                   inputTensor.second.GetMemoryArea());
+            // Check if the input memory can be imported
+            if (outputTensorHandle->CanBeImported(passThroughTensorHandle->Map(), forceImportMemorySource))
+            {
+                passThroughTensorHandle->Unmap();
+                if (outputTensorHandle->Import(passThroughTensorHandle->Map(), forceImportMemorySource))
+                {
+                    passThroughTensorHandle->Unmap();
+                    try
+                    {
+                        m_WorkloadQueue[m_InputWorkloadSlotPairs[layerBindingId].first].get()->ReplaceInputTensorHandle(
+                            outputTensorHandle, m_InputWorkloadSlotPairs[layerBindingId].second);
+                        importedInputs.push_back(m_CurImportedInputId++);
+                        // For force import, we want OutputHandler to own the TensorHandle,
+                        // so we do not move the TensorHandle to m_PreImportedInputHandles as in AsyncEnabled networks
+                        ImportedTensorHandlePin importedTensorHandlePin{layerBindingId, nullptr};
+                        m_PreImportedInputHandles.push_back(std::move(importedTensorHandlePin));
+                    }
+                    catch(armnn::UnimplementedException& e)
+                    {
+                        IgnoreUnused(e);
+                        // Method not implement, cannot use import tensor and have to use allocated data instead
+                        handler.UseAllocatedData();
+                    }
+                }
+            }
+            else
+            {
+                // Cannot import, use allocated data
+                handler.UseAllocatedData();
+            }
+
+        }
+
+        return importedInputs;
     }
-
-    std::vector<ImportedInputId> importedInputs;
-    Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
-
-    for (auto inputTensor : inputTensors)
+    else
     {
-        auto layerBindingId = inputTensor.first;
-        auto it = std::find_if(graph.GetInputLayers().begin(), graph.GetInputLayers().end(), [=](auto* layer)
-        {
-            return layer->GetBindingId() == layerBindingId;
-        });
+        // Import when the import of network properties is enabled
+        std::vector<ImportedInputId> importedInputs;
+        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
 
-        if (it == graph.GetInputLayers().end())
+        for (auto inputTensor : inputTensors)
         {
-            throw MemoryImportException(fmt::format("ImportInputs: Memory Import failed, unknown LayerBindingId: {}",
-                                                    layerBindingId));
+            auto layerBindingId = inputTensor.first;
+            auto it = std::find_if(graph.GetInputLayers().begin(), graph.GetInputLayers().end(), [=](auto* layer)
+            {
+                return layer->GetBindingId() == layerBindingId;
+            });
+
+            if (it == graph.GetInputLayers().end())
+            {
+                throw MemoryImportException(fmt::format(
+                    "ImportInputs: Memory Import failed, unknown LayerBindingId: {}", layerBindingId));
+            }
+
+            const Layer* layer = *it;
+            if (layer->GetType() != LayerType::Input)
+            {
+                throw InvalidArgumentException("ImportInputs: given layer not an InputLayer");
+            }
+
+            auto& backend = m_Backends.at(layer->GetBackendId());
+            if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
+            {
+                std::string er = backend->GetId();
+                er += " does not have PreImportIOTensors capability";
+                throw BackendCapabilityException(er);
+            }
+
+            const OutputSlot& outputSlot = layer->GetOutputSlots()[0];
+
+            ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
+            const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();
+
+            ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
+            ARMNN_ASSERT(handleFactory);
+
+            ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
+                                                            handleFactory->CreateTensorHandle(tensorInfo, false)};
+
+            ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();
+
+            if (!CheckFlag(tensorHandle->GetImportFlags(), m_NetworkProperties.m_InputSource))
+            {
+                throw MemoryImportException(
+                    fmt::format("ImportInputs: Memory Import failed, backend: "
+                                "{} does not support importing from source {}"
+                                , factoryId, m_NetworkProperties.m_InputSource));
+            }
+
+            std::unique_ptr<ITensorHandle> passThroughTensorHandle =
+                    std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
+                                                                   inputTensor.second.GetMemoryArea());
+
+            if (tensorHandle->Import(passThroughTensorHandle->Map(), m_NetworkProperties.m_InputSource))
+            {
+                importedInputs.push_back(m_CurImportedInputId++);
+                passThroughTensorHandle->Unmap();
+            }
+            else
+            {
+                passThroughTensorHandle->Unmap();
+                throw MemoryImportException("ImportInputs: Memory Import failed");
+            }
+
+            m_PreImportedInputHandles.push_back(std::move(importedTensorHandlePin));
         }
-
-        const Layer* layer = *it;
-        if (layer->GetType() != LayerType::Input)
-        {
-            throw InvalidArgumentException("ImportInputs: given layer not an InputLayer");
-        }
-
-        auto& backend = m_Backends.at(layer->GetBackendId());
-        if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
-        {
-            std::string er = backend->GetId();
-            er += " does not have PreImportIOTensors capability";
-            throw BackendCapabilityException(er);
-        }
-
-        const OutputSlot& outputSlot = layer->GetOutputSlots()[0];
-
-        ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
-        const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();
-
-        ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
-        ARMNN_ASSERT(handleFactory);
-
-        ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
-                                                        handleFactory->CreateTensorHandle(tensorInfo, false)};
-
-        ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();
-
-        if (!CheckFlag(tensorHandle->GetImportFlags(), m_NetworkProperties.m_InputSource))
-        {
-            throw MemoryImportException(
-                fmt::format("ImportInputs: Memory Import failed, backend: {} does not support importing from source {}"
-                            , factoryId, m_NetworkProperties.m_InputSource));
-        }
-
-        std::unique_ptr<ITensorHandle> passThroughTensorHandle =
-                std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
-                                                               inputTensor.second.GetMemoryArea());
-
-        if (tensorHandle->Import(passThroughTensorHandle->Map(), m_NetworkProperties.m_InputSource))
-        {
-            importedInputs.push_back(m_CurImportedInputId++);
-            passThroughTensorHandle->Unmap();
-        }
-        else
-        {
-            passThroughTensorHandle->Unmap();
-            throw MemoryImportException("ImportInputs: Memory Import failed");
-        }
-
-        m_PreImportedInputHandles.push_back(std::move(importedTensorHandlePin));
+        return importedInputs;
     }
-
-    return importedInputs;
 }
 
-std::vector<ImportedOutputId> LoadedNetwork::ImportOutputs(const OutputTensors& outputTensors)
+std::vector<ImportedOutputId> LoadedNetwork::ImportOutputs(const OutputTensors& outputTensors,
+                                                           MemorySource forceImportMemorySource)
 {
-    if (!m_NetworkProperties.m_ExportEnabled)  // Try import the output tensor
+    if (!m_NetworkProperties.m_ExportEnabled)
     {
-        throw MemoryImportException("ImportOutputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
+        // Cannot import if import is not enabled and forceImportMemorySource is undefined
+        if (forceImportMemorySource == MemorySource::Undefined)
+        {
+            throw MemoryImportException("ImportOutputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
+        }
+        // If forceImportMemorySource is defined, try import if memory is aligned
+        if (outputTensors.size() != m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumOutputs())
+        {
+            throw MemoryImportException("ImportOutputs: Force Import failed, incorrect number of tensors");
+        }
+        std::vector<ImportedInputId> importedOutputs;
+        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
+        for (auto outputTensor : outputTensors)
+        {
+            auto layerBindingId = outputTensor.first;
+            auto it = std::find_if(graph.GetOutputLayers().begin(), graph.GetOutputLayers().end(), [=](auto* layer)
+            {
+                return layer->GetBindingId() == layerBindingId;
+            });
+
+            if (it == graph.GetOutputLayers().end())
+            {
+                throw MemoryImportException(fmt::format("ImportOutputs: Memory Import failed, "
+                                                        "unknown LayerBindingId: {}",
+                                                        layerBindingId));
+            }
+
+            const Layer* layer = *it;
+            if (layer->GetType() != LayerType::Output)
+            {
+                throw InvalidArgumentException("ImportOutputs: given layer not an OutputLayer");
+            }
+
+            const OutputSlot* outputSlot = layer->GetInputSlots()[0].GetConnectedOutputSlot();
+            ITensorHandleFactory::FactoryId factoryId = outputSlot->GetTensorHandleFactoryId();
+            ITensorHandleFactory::FactoryId importFactoryId =
+                m_TensorHandleFactoryRegistry.GetMatchingImportFactoryId(factoryId);
+            ITensorHandleFactory* importFactory =
+                m_TensorHandleFactoryRegistry.GetFactory(importFactoryId, forceImportMemorySource);
+            if (!importFactory)
+            {
+                throw MemoryImportException("ImportOutputs: Force Import failed, cannot find matching Import Factory");
+            }
+
+            OutputHandler& outputHandler =
+                const_cast<OutputHandler&>(layer->GetInputSlots()[0].GetConnectedOutputSlot()->GetOutputHandler());
+            outputHandler.SetAllocatedData();
+            ITensorHandle* inputTensorHandle = outputHandler.GetData();
+            outputHandler.CreateTensorHandles(*importFactory, false);
+            inputTensorHandle = outputHandler.GetData();
+
+            // Check if the output memory can be imported
+            if (inputTensorHandle->CanBeImported(outputTensor.second.GetMemoryArea(), forceImportMemorySource))
+            {
+                if (inputTensorHandle->Import(outputTensor.second.GetMemoryArea(), forceImportMemorySource))
+                {
+                    try
+                    {
+                        m_WorkloadQueue[m_OutputWorkloadSlotPairs[layerBindingId].first].get()->
+                            ReplaceOutputTensorHandle(inputTensorHandle,
+                                                      m_OutputWorkloadSlotPairs[layerBindingId].second);
+                        importedOutputs.push_back(m_CurImportedOutputId++);
+                        // For force import, we want OutputHandler to own the TensorHandle,
+                        // so we do not move the TensorHandle to m_PreImportedOutputHandles as in AsyncEnabled networks
+                        ImportedTensorHandlePin importedTensorHandlePin{layerBindingId, nullptr};
+                        m_PreImportedOutputHandles.push_back(std::move(importedTensorHandlePin));
+                    }
+                    catch(armnn::UnimplementedException& e)
+                    {
+                        IgnoreUnused(e);
+                        // Method not implement, cannot use import tensor and have to use allocated data instead
+                        outputHandler.UseAllocatedData();
+                    }
+                }
+            }
+            else
+            {
+                // Cannot import, use allocated memory
+                outputHandler.UseAllocatedData();
+            }
+        }
+        return importedOutputs;
     }
 
     std::vector<ImportedOutputId> importedOutputs;
diff --git a/src/armnn/LoadedNetwork.hpp b/src/armnn/LoadedNetwork.hpp
index 9de6307..f637dec 100644
--- a/src/armnn/LoadedNetwork.hpp
+++ b/src/armnn/LoadedNetwork.hpp
@@ -55,14 +55,18 @@
     TensorInfo GetInputTensorInfo(LayerBindingId layerId) const;
     TensorInfo GetOutputTensorInfo(LayerBindingId layerId) const;
 
-    std::vector<ImportedInputId> ImportInputs(const InputTensors& inputTensors);
-    std::vector<ImportedOutputId> ImportOutputs(const OutputTensors& outputTensors);
+    std::vector<ImportedInputId> ImportInputs(const InputTensors& inputTensors,
+                                              MemorySource forceImportMemorySource = MemorySource::Undefined);
+    std::vector<ImportedOutputId> ImportOutputs(const OutputTensors& outputTensors,
+                                                MemorySource forceImportMemorySource = MemorySource::Undefined);
 
     void ClearImportedInputs(const std::vector<ImportedInputId> inputIds);
     void ClearImportedOutputs(const std::vector<ImportedOutputId> outputIds);
 
     /// Single thread execution of the loaded network
-    Status EnqueueWorkload(const InputTensors& inputTensors, const OutputTensors& outputTensors);
+    Status EnqueueWorkload(const InputTensors& inputTensors, const OutputTensors& outputTensors,
+                           std::vector<ImportedInputId> preImportedInputIds = {},
+                           std::vector<ImportedOutputId> preImportedOutputIds = {});
 
     /// Thread safe execution of the loaded network
     Status Execute(const InputTensors& inputTensors,
@@ -200,8 +204,9 @@
 
     // A set of vectors to record the workload queue indexes and their corresponding Input/Output Slot indexes
     // which are connected to Inputs and Outputs for the network.
-    std::vector<std::pair<unsigned int, unsigned int>> m_InputWorkloadSlotPairs;
-    std::vector<std::pair<unsigned int, unsigned int>> m_OutputWorkloadSlotPairs;
+    std::unordered_map<LayerBindingId, std::pair<unsigned int, unsigned int>> m_InputWorkloadSlotPairs;
+    std::unordered_map<LayerBindingId, std::pair<unsigned int, unsigned int>> m_OutputWorkloadSlotPairs;
+
 };
 
 }
diff --git a/src/armnn/OutputHandler.hpp b/src/armnn/OutputHandler.hpp
index 41a49af..3fd2519 100644
--- a/src/armnn/OutputHandler.hpp
+++ b/src/armnn/OutputHandler.hpp
@@ -50,10 +50,15 @@
 
     void SetData(std::unique_ptr<ITensorHandle> data) { m_TensorHandle = std::move(data); }
 
+    void SetAllocatedData() { m_AllocatedTensorHandle = std::move(m_TensorHandle); }
+
+    void UseAllocatedData() { m_TensorHandle = std::move(m_AllocatedTensorHandle); }
+
     /// @brief Returns true if SetTensorInfo() has been called at least once on this.
     bool IsTensorInfoSet() const { return m_bTensorInfoSet; }
 private:
     std::unique_ptr<ITensorHandle> m_TensorHandle;
+    std::unique_ptr<ITensorHandle> m_AllocatedTensorHandle;
     TensorInfo m_TensorInfo;
     bool m_bTensorInfoSet = false;
 };
diff --git a/src/armnn/Runtime.cpp b/src/armnn/Runtime.cpp
index 2752e72..95fb8a3 100644
--- a/src/armnn/Runtime.cpp
+++ b/src/armnn/Runtime.cpp
@@ -77,14 +77,16 @@
     return pRuntimeImpl->GetOutputTensorInfo(networkId, layerId);
 }
 
-std::vector<ImportedInputId> IRuntime::ImportInputs(NetworkId networkId, const InputTensors& inputTensors)
+std::vector<ImportedInputId> IRuntime::ImportInputs(NetworkId networkId, const InputTensors& inputTensors,
+                                                    MemorySource forceImportMemorySource)
 {
-    return pRuntimeImpl->ImportInputs(networkId, inputTensors);
+    return pRuntimeImpl->ImportInputs(networkId, inputTensors, forceImportMemorySource);
 }
 
-std::vector<ImportedOutputId> IRuntime::ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors)
+std::vector<ImportedOutputId> IRuntime::ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors,
+                                                      MemorySource forceImportMemorySource)
 {
-    return pRuntimeImpl->ImportOutputs(networkId, outputTensors);
+    return pRuntimeImpl->ImportOutputs(networkId, outputTensors, forceImportMemorySource);
 }
 
 void IRuntime::ClearImportedInputs(NetworkId networkId, const std::vector<ImportedInputId> inputIds)
@@ -98,9 +100,12 @@
 
 Status IRuntime::EnqueueWorkload(NetworkId networkId,
                                  const InputTensors& inputTensors,
-                                 const OutputTensors& outputTensors)
+                                 const OutputTensors& outputTensors,
+                                 std::vector<ImportedInputId> preImportedInputIds,
+                                 std::vector<ImportedOutputId> preImportedOutputIds)
 {
-    return pRuntimeImpl->EnqueueWorkload(networkId, inputTensors, outputTensors);
+    return pRuntimeImpl->EnqueueWorkload(networkId, inputTensors, outputTensors,
+                                         preImportedInputIds, preImportedOutputIds);
 }
 
 Status IRuntime::Execute(IWorkingMemHandle& workingMemHandle,
@@ -566,14 +571,16 @@
     return GetLoadedNetworkPtr(networkId)->GetOutputTensorInfo(layerId);
 }
 
-std::vector<ImportedInputId> RuntimeImpl::ImportInputs(NetworkId networkId, const InputTensors& inputTensors)
+std::vector<ImportedInputId> RuntimeImpl::ImportInputs(NetworkId networkId, const InputTensors& inputTensors,
+                                                       MemorySource forceImportMemorySource)
 {
-    return GetLoadedNetworkPtr(networkId)->ImportInputs(inputTensors);
+    return GetLoadedNetworkPtr(networkId)->ImportInputs(inputTensors, forceImportMemorySource);
 }
 
-std::vector<ImportedOutputId> RuntimeImpl::ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors)
+std::vector<ImportedOutputId> RuntimeImpl::ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors,
+                                                         MemorySource forceImportMemorySource)
 {
-    return GetLoadedNetworkPtr(networkId)->ImportOutputs(outputTensors);
+    return GetLoadedNetworkPtr(networkId)->ImportOutputs(outputTensors, forceImportMemorySource);
 }
 
 void RuntimeImpl::ClearImportedInputs(NetworkId networkId, const std::vector<ImportedInputId> inputIds)
@@ -587,7 +594,9 @@
 
 Status RuntimeImpl::EnqueueWorkload(NetworkId networkId,
                                 const InputTensors& inputTensors,
-                                const OutputTensors& outputTensors)
+                                const OutputTensors& outputTensors,
+                                std::vector<ImportedInputId> preImportedInputIds,
+                                std::vector<ImportedOutputId> preImportedOutputIds)
 {
     const auto startTime = armnn::GetTimeNow();
 
@@ -617,7 +626,8 @@
     }
     lastId=networkId;
 
-    auto status = loadedNetwork->EnqueueWorkload(inputTensors, outputTensors);
+    auto status = loadedNetwork->EnqueueWorkload(inputTensors, outputTensors,
+                                                 preImportedInputIds, preImportedOutputIds);
 
     ARMNN_LOG(info) << "Execution time: " << std::setprecision(2)
                     << std::fixed << armnn::GetTimeDuration(startTime).count() << " ms.";
diff --git a/src/armnn/Runtime.hpp b/src/armnn/Runtime.hpp
index 4052bb6..bd37013 100644
--- a/src/armnn/Runtime.hpp
+++ b/src/armnn/Runtime.hpp
@@ -55,8 +55,10 @@
     armnn::TensorInfo GetInputTensorInfo(NetworkId networkId, LayerBindingId layerId) const;
     armnn::TensorInfo GetOutputTensorInfo(NetworkId networkId, LayerBindingId layerId) const;
 
-    std::vector<ImportedInputId> ImportInputs(NetworkId networkId, const InputTensors& inputTensors);
-    std::vector<ImportedOutputId> ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors);
+    std::vector<ImportedInputId> ImportInputs(NetworkId networkId, const InputTensors& inputTensors,
+                                              MemorySource forceImportMemorySource = MemorySource::Undefined);
+    std::vector<ImportedOutputId> ImportOutputs(NetworkId networkId, const OutputTensors& outputTensors,
+                                                MemorySource forceImportMemorySource = MemorySource::Undefined);
 
     void ClearImportedInputs(NetworkId networkId, const std::vector<ImportedInputId> inputIds);
     void ClearImportedOutputs(NetworkId networkId, const std::vector<ImportedOutputId> outputIds);
@@ -64,7 +66,9 @@
     // Evaluates network using input in inputTensors, outputs filled into outputTensors.
     Status EnqueueWorkload(NetworkId networkId,
                            const InputTensors& inputTensors,
-                           const OutputTensors& outputTensors);
+                           const OutputTensors& outputTensors,
+                           std::vector<ImportedInputId> preImportedInputIds = {},
+                           std::vector<ImportedOutputId> preImportedOutputIds = {});
 
     /// This is an experimental function.
     /// Evaluates a network using input in inputTensors and outputs filled into outputTensors.
diff --git a/src/backends/cl/ClBackend.cpp b/src/backends/cl/ClBackend.cpp
index 8abb16c..0fc5da7 100644
--- a/src/backends/cl/ClBackend.cpp
+++ b/src/backends/cl/ClBackend.cpp
@@ -133,6 +133,15 @@
     MemorySourceFlags inputFlags,
     MemorySourceFlags outputFlags) const
 {
+    // To allow force import if inputFlags/outputFlags are Undefined, set it as Malloc
+    if (inputFlags == static_cast<MemorySourceFlags>(MemorySource::Undefined))
+    {
+        inputFlags = static_cast<MemorySourceFlags>(MemorySource::Malloc);
+    }
+    if (outputFlags == static_cast<MemorySourceFlags>(MemorySource::Undefined))
+    {
+        outputFlags = static_cast<MemorySourceFlags>(MemorySource::Malloc);
+    }
     std::shared_ptr<ClMemoryManager> memoryManager;
     if (m_UsingCustomAllocator)
     {
@@ -193,6 +202,15 @@
                                               MemorySourceFlags inputFlags,
                                               MemorySourceFlags outputFlags)
 {
+    // To allow force import if inputFlags/outputFlags are Undefined, set it as Malloc
+    if (inputFlags == static_cast<MemorySourceFlags>(MemorySource::Undefined))
+    {
+        inputFlags = static_cast<MemorySourceFlags>(MemorySource::Malloc);
+    }
+    if (outputFlags == static_cast<MemorySourceFlags>(MemorySource::Undefined))
+    {
+        outputFlags = static_cast<MemorySourceFlags>(MemorySource::Malloc);
+    }
     std::shared_ptr<ClMemoryManager> memoryManager;
     if (m_UsingCustomAllocator)
     {
diff --git a/src/backends/cl/ClImportTensorHandle.hpp b/src/backends/cl/ClImportTensorHandle.hpp
index a236a70..54710d8 100644
--- a/src/backends/cl/ClImportTensorHandle.hpp
+++ b/src/backends/cl/ClImportTensorHandle.hpp
@@ -205,7 +205,11 @@
                 // We do this to match the behaviour of the Import function later on.
                 auto cachelineAlignment =
                         arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
-                auto roundedSize = cachelineAlignment + totalBytes - (totalBytes % cachelineAlignment);
+                auto roundedSize = totalBytes;
+                if (totalBytes % cachelineAlignment != 0)
+                {
+                    roundedSize = cachelineAlignment + totalBytes - (totalBytes % cachelineAlignment);
+                }
 
                 cl_int error = CL_SUCCESS;
                 cl_mem buffer;
@@ -252,7 +256,11 @@
         // This does not change the size of the buffer, only the size of the mapping the buffer is mapped to
         auto cachelineAlignment =
                 arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
-        auto roundedSize = cachelineAlignment + totalBytes - (totalBytes % cachelineAlignment);
+        auto roundedSize = totalBytes;
+        if (totalBytes % cachelineAlignment != 0)
+        {
+            roundedSize = cachelineAlignment + totalBytes - (totalBytes % cachelineAlignment);
+        }
 
         cl_int error = CL_SUCCESS;
         cl_mem buffer;
diff --git a/src/backends/cl/test/ClCreateWorkloadTests.cpp b/src/backends/cl/test/ClCreateWorkloadTests.cpp
index d8b2d4f..4a28205 100644
--- a/src/backends/cl/test/ClCreateWorkloadTests.cpp
+++ b/src/backends/cl/test/ClCreateWorkloadTests.cpp
@@ -11,11 +11,14 @@
 #include <armnn/utility/PolymorphicDowncast.hpp>
 #include <armnn/backends/MemCopyWorkload.hpp>
 #include <armnnTestUtils/TensorCopyUtils.hpp>
+#include <TensorHelpers.hpp>
 #include <armnnTestUtils/WorkloadTestUtils.hpp>
 
 #include <aclCommon/test/CreateWorkloadClNeon.hpp>
 #include <aclCommon/ArmComputeTensorUtils.hpp>
 
+#include <cl/ClImportTensorHandle.hpp>
+#include <cl/ClImportTensorHandleFactory.hpp>
 #include <cl/ClTensorHandle.hpp>
 #include <cl/ClWorkloadFactory.hpp>
 #include <cl/workloads/ClWorkloads.hpp>
@@ -355,6 +358,64 @@
     ARMNN_ASSERT(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::WINOGRAD);
 }
 
+TEST_CASE_FIXTURE(ClContextControlFixture, "ClReplaceInputOutputConvolution2dWorkload")
+{
+    // Create Convolution2dWorkload with ClTensorHandle input and output
+    // Then replace the input and output with ClImportTensorHandle
+    Graph graph;
+    ClWorkloadFactory factory =
+        ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager());
+
+    auto workload =
+        CreateConvolution2dWorkloadTest<ClConvolution2dWorkload, DataType::Float32>(factory,
+                                                                                    graph,
+                                                                                    DataLayout::NHWC);
+
+    TensorShape inputShape  = std::initializer_list<unsigned int>({2, 8, 16, 3});
+    TensorShape outputShape = std::initializer_list<unsigned int>({2, 2, 10, 2});
+
+    // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest).
+    Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
+    auto inputHandle  = PolymorphicDowncast<ITensorHandle*>(queueDescriptor.m_Inputs[0]);
+    auto outputHandle = PolymorphicDowncast<ITensorHandle*>(queueDescriptor.m_Outputs[0]);
+    CHECK((inputHandle->GetShape() == inputShape));
+    CHECK((outputHandle->GetShape() == outputShape));
+    // The input and output handles are created correctly as ClTensorHandle
+    CHECK((dynamic_cast<ClTensorHandle*>(inputHandle) != nullptr));
+    CHECK((dynamic_cast<ClTensorHandle*>(outputHandle) != nullptr));
+
+    // Replace with ImportTensorHandle
+    ClImportTensorHandleFactory importFactory(static_cast<MemorySourceFlags>(MemorySource::Malloc),
+                                              static_cast<MemorySourceFlags>(MemorySource::Malloc));
+
+    TensorInfo inputInfo({ 2, 8, 16, 3 }, DataType::Float32);
+    TensorInfo outputInfo({ 2, 2, 10, 2 }, DataType::Float32);
+
+    // create TensorHandle for memory import
+    auto inputImportHandle = importFactory.CreateTensorHandle(inputInfo);
+    auto outputImportHandle = importFactory.CreateTensorHandle(outputInfo);
+
+    // Calling ReplaceInputTensorHandle and ReplaceOutputTensorHandle does not throw exception
+    // as Reconfigure function is implemented
+    workload->ReplaceInputTensorHandle(inputImportHandle.get(), 0);
+    workload->ReplaceOutputTensorHandle(outputImportHandle.get(), 0);
+
+    // Correctly replaced with the import handles with correct information
+    queueDescriptor = workload->GetData();
+    auto replacedInputHandle  = PolymorphicDowncast<ITensorHandle*>(queueDescriptor.m_Inputs[0]);
+    auto replacedOutputHandle = PolymorphicDowncast<ITensorHandle*>(queueDescriptor.m_Outputs[0]);
+    CHECK((replacedInputHandle->GetShape() == inputShape));
+    CHECK((replacedOutputHandle->GetShape() == outputShape));
+
+    CHECK((inputImportHandle.get() == replacedInputHandle));
+    CHECK((inputImportHandle.get() == replacedInputHandle));
+
+    CHECK((dynamic_cast<ClTensorHandle*>(replacedInputHandle) == nullptr));
+    CHECK((dynamic_cast<ClImportTensorHandle*>(replacedInputHandle) != nullptr));
+    CHECK((dynamic_cast<ClTensorHandle*>(replacedOutputHandle) == nullptr));
+    CHECK((dynamic_cast<ClImportTensorHandle*>(replacedOutputHandle) != nullptr));
+}
+
 TEST_CASE_FIXTURE(ClContextControlFixture, "CreateConvolution2dClCompiledContextWorkload")
 {
     using namespace armnn;
diff --git a/src/backends/cl/test/ClImportTensorHandleTests.cpp b/src/backends/cl/test/ClImportTensorHandleTests.cpp
index 3d70264..1617654 100644
--- a/src/backends/cl/test/ClImportTensorHandleTests.cpp
+++ b/src/backends/cl/test/ClImportTensorHandleTests.cpp
@@ -274,4 +274,157 @@
     // we can be confident that it will be successfully imported. All other cases will need to be handled by the user.
 }
 
+TEST_CASE_FIXTURE(ClContextControlFixture, "ClForceImportConv2dEndToEnd")
+{
+    // Create runtime in which test will run
+    IRuntime::CreationOptions options;
+    IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+    // build up the structure of the network
+    INetworkPtr network(INetwork::Create());
+
+    armnn::TensorInfo inputInfo({ 1, 3, 4, 1 }, DataType::Float32);
+    armnn::TensorInfo kernelInfo({ 1, 3, 3, 1 }, DataType::Float32);
+    armnn::TensorInfo outputInfo({ 1, 3, 4, 1 }, DataType::Float32);
+
+    kernelInfo.SetConstant(true);
+
+    std::vector<float> kernel =
+    {
+        4, 5, 6,
+        0, 0, 0,
+        3, 2, 1
+    };
+
+    const std::vector<float> expectedOutput =
+    {
+        23, 41, 33, 21,
+        44, 65, 76, 52,
+        82, 85, 79, 42
+    };
+
+    unsigned int numElements = inputInfo.GetNumElements();
+    size_t totalBytes = numElements * sizeof(float);
+
+    IConnectableLayer* const inputLayer = network->AddInputLayer(0, "input");
+    ARMNN_ASSERT(inputLayer);
+
+    armnn::ConstTensor weights(kernelInfo, kernel);
+
+    armnn::Convolution2dDescriptor convDesc2d;
+    convDesc2d.m_StrideX = 1;
+    convDesc2d.m_StrideY = 1;
+    convDesc2d.m_PadLeft = 1;
+    convDesc2d.m_PadRight = 1;
+    convDesc2d.m_PadTop = 1;
+    convDesc2d.m_PadBottom = 1;
+    convDesc2d.m_DataLayout = DataLayout::NHWC;
+    armnn::IConnectableLayer* const convLayer = network->AddConvolution2dLayer(convDesc2d,
+                                                                          weights,
+                                                                          armnn::EmptyOptional(),
+                                                                          "conv");
+    ARMNN_ASSERT(convLayer);
+
+    inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+    IConnectableLayer* output = network->AddOutputLayer(0, "output");
+    convLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+    convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    // Optimize the network
+    OptimizerOptions optOptions;
+    optOptions.m_ImportEnabled = false;
+    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+    IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optOptions);
+    CHECK(optNet);
+
+    // Loads it into the runtime.
+    NetworkId netId;
+    std::string ignoredErrorMessage;
+    // Enable Importing
+    INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined);
+    runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);
+
+    // Creates structures for input & output
+    const size_t alignment =
+        arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
+    size_t space = totalBytes + alignment + alignment;
+    auto inputData = std::make_unique<uint8_t[]>(space);
+    void* alignedInputPtr = inputData.get();
+    CHECK(std::align(alignment, totalBytes, alignedInputPtr, space));
+
+    // Input with negative values
+    auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr);
+    inputPtr[0] = 1;
+    inputPtr[1] = 5;
+    inputPtr[2] = 2;
+    inputPtr[3] = 3;
+    inputPtr[4] = 8;
+    inputPtr[5] = 7;
+    inputPtr[6] = 3;
+    inputPtr[7] = 6;
+    inputPtr[8] = 3;
+    inputPtr[9] = 3;
+    inputPtr[10] = 9;
+    inputPtr[11] = 1;
+
+
+    auto outputData = std::make_unique<uint8_t[]>(space);
+    void* alignedOutputPtr = outputData.get();
+    CHECK(std::align(alignment, totalBytes, alignedOutputPtr, space));
+    auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr);
+    std::fill_n(outputPtr, numElements, -10.0f);
+
+    TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(netId, 0);
+    inputTensorInfo.SetConstant(true);
+    InputTensors inputTensors
+    {
+        {0,armnn::ConstTensor(inputTensorInfo, alignedInputPtr)},
+    };
+    OutputTensors outputTensors
+    {
+        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputPtr)}
+    };
+
+    runtime->GetProfiler(netId)->EnableProfiling(true);
+
+    INFO("Run ImportInputs");
+    std::vector<ImportedInputId> importedInputIds =
+        runtime->ImportInputs(netId, inputTensors, MemorySource::Malloc);
+    std::vector<ImportedOutputId> importedOutputIds =
+        runtime->ImportOutputs(netId, outputTensors, MemorySource::Malloc);
+
+    // Do the inference
+    runtime->EnqueueWorkload(netId, inputTensors, outputTensors, importedInputIds, importedOutputIds);
+
+    // Retrieve the Profiler.Print() output to get the workload execution
+    ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance();
+    std::stringstream ss;
+    profilerManager.GetProfiler()->Print(ss);;
+    std::string dump = ss.str();
+
+    // Contains Convolution2dWorkload
+    std::size_t found = dump.find("Convolution2dWorkload");
+    CHECK(found != std::string::npos);
+
+    // Contains SyncMemGeneric
+    found = dump.find("SyncMemGeneric");
+    CHECK(found != std::string::npos);
+
+    // Does not contain CopyMemGeneric
+    found = dump.find("CopyMemGeneric");
+    CHECK(found == std::string::npos);
+
+    runtime->UnloadNetwork(netId);
+
+    // Check output is as expected
+    // Validate result by checking that the output has no negative values
+    auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr);
+    CHECK(outputResult);
+
+    // Check the output is correct
+    CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
+}
+
 }
diff --git a/src/backends/cl/workloads/ClBaseWorkload.hpp b/src/backends/cl/workloads/ClBaseWorkload.hpp
index e74fc84..03417e3 100644
--- a/src/backends/cl/workloads/ClBaseWorkload.hpp
+++ b/src/backends/cl/workloads/ClBaseWorkload.hpp
@@ -20,17 +20,38 @@
     // Replace input tensor handle with the given TensorHandle and call Reconfigure()
     void ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override
     {
+        ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
         this->m_Data.m_Inputs[slot] = tensorHandle;
-        Reconfigure();
+        try
+        {
+            Reconfigure();
+        }
+        catch(armnn::UnimplementedException& e)
+        {
+            // Cannot reconfigure, revert the slot back and throw the exception.
+            this->m_Data.m_Inputs[slot] = backupHandle;
+            throw e;
+        }
     }
 
     // Replace output tensor handle with the given TensorHandle and call Reconfigure()
     void ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override
     {
+        ITensorHandle* backupHandle = this->m_Data.m_Outputs[slot];
         this->m_Data.m_Outputs[slot] = tensorHandle;
-        Reconfigure();
+        try
+        {
+            Reconfigure();
+        }
+        catch(armnn::UnimplementedException& e)
+        {
+            // Cannot reconfigure, revert the slot back and throw the exception.
+            this->m_Data.m_Inputs[slot] = backupHandle;
+            throw e;
+        }
     }
 
+protected:
     // Reconfigure the workload configuration. Throw armnn::UnimplementedException by default.
     virtual void Reconfigure()
     {
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
index 705e92d..cdfa885 100644
--- a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
+++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
@@ -90,6 +90,10 @@
     arm_compute::ICLTensor& input  = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
     arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
 
+    // Create Proxy tensor and set the initial tensor handle to it
+    m_InputProxy = std::make_unique<ICLTensorProxy>(&input);
+    m_OutputProxy = std::make_unique<ICLTensorProxy>(&output);
+
     arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
     input.info()->set_data_layout(aclDataLayout);
     output.info()->set_data_layout(aclDataLayout);
@@ -101,10 +105,10 @@
     {
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_configure");
         m_ConvolutionLayer.configure(clCompileContext,
-                                     &input,
+                                     m_InputProxy.get(),
                                      m_KernelTensor.get(),
                                      m_BiasTensor.get(),
-                                     &output,
+                                     m_OutputProxy.get(),
                                      padStrideInfo,
                                      arm_compute::WeightsInfo(),
                                      aclDilationInfo,
@@ -174,4 +178,13 @@
     FreeTensorIfUnused(m_BiasTensor);
 }
 
+void ClConvolution2dWorkload::Reconfigure()
+{
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_Reconfigure");
+    arm_compute::ICLTensor& input  = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+    m_InputProxy->set(&input);
+    m_OutputProxy->set(&output);
+}
+
 } //namespace armnn
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.hpp b/src/backends/cl/workloads/ClConvolution2dWorkload.hpp
index 8a4599d..891d509 100644
--- a/src/backends/cl/workloads/ClConvolution2dWorkload.hpp
+++ b/src/backends/cl/workloads/ClConvolution2dWorkload.hpp
@@ -13,6 +13,8 @@
 #include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h>
 #include <arm_compute/runtime/MemoryManagerOnDemand.h>
 
+#include <cl/ICLTensorProxy.hpp>
+
 #include <memory>
 
 namespace armnn
@@ -38,6 +40,9 @@
 
     arm_compute::ConvolutionMethod GetConvolutionMethod() const;
 
+protected:
+    void Reconfigure() override;
+
 private:
     mutable arm_compute::CLConvolutionLayer m_ConvolutionLayer;
 
@@ -47,6 +52,9 @@
     arm_compute::ConvolutionMethod m_ConvolutionMethod;
 
     void FreeUnusedTensors();
+
+    std::unique_ptr<ICLTensorProxy> m_InputProxy;
+    std::unique_ptr<ICLTensorProxy> m_OutputProxy;
 };
 
 } //namespace armnn
diff --git a/src/backends/neon/workloads/NeonBaseWorkload.hpp b/src/backends/neon/workloads/NeonBaseWorkload.hpp
index a92f35a..63f3539 100644
--- a/src/backends/neon/workloads/NeonBaseWorkload.hpp
+++ b/src/backends/neon/workloads/NeonBaseWorkload.hpp
@@ -20,17 +20,38 @@
     // Replace input tensor handle with the given TensorHandle and call Reconfigure()
     void ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override
     {
+        ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
         this->m_Data.m_Inputs[slot] = tensorHandle;
-        Reconfigure();
+        try
+        {
+            Reconfigure();
+        }
+        catch(armnn::UnimplementedException& e)
+        {
+            // Cannot reconfigure, revert the slot back and throw the exception.
+            this->m_Data.m_Inputs[slot] = backupHandle;
+            throw e;
+        }
     }
 
     // Replace output tensor handle with the given TensorHandle and call Reconfigure()
     void ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override
     {
+        ITensorHandle* backupHandle = this->m_Data.m_Outputs[slot];
         this->m_Data.m_Outputs[slot] = tensorHandle;
-        Reconfigure();
+        try
+        {
+            Reconfigure();
+        }
+        catch(armnn::UnimplementedException& e)
+        {
+            // Cannot reconfigure, revert the slot back and throw the exception.
+            this->m_Data.m_Inputs[slot] = backupHandle;
+            throw e;
+        }
     }
 
+protected:
     // Reconfigure the workload configuration. Throw armnn::UnimplementedException by default.
     virtual void Reconfigure()
     {
diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp
index 4444f5c..7a6cf97 100644
--- a/src/backends/reference/test/RefEndToEndTests.cpp
+++ b/src/backends/reference/test/RefEndToEndTests.cpp
@@ -1378,6 +1378,93 @@
     RankEndToEnd<armnn::DataType::QSymmS8>(defaultBackends);
 }
 
+TEST_CASE("RefForceImportTest")
+{
+    using namespace armnn;
+
+    std::vector<BackendId> backends = defaultBackends;
+
+    IRuntime::CreationOptions options;
+    IRuntimePtr runtime(IRuntime::Create(options));
+
+    // Builds up the structure of the network.
+    INetworkPtr net(INetwork::Create());
+
+    IConnectableLayer* input = net->AddInputLayer(0);
+
+    ActivationDescriptor descriptor;
+    descriptor.m_Function = ActivationFunction::Square;
+    IConnectableLayer* activationLayer = net->AddActivationLayer(descriptor);
+
+    IConnectableLayer* output = net->AddOutputLayer(0);
+
+    input->GetOutputSlot(0).Connect(activationLayer->GetInputSlot(0));
+    activationLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+    input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 1, 4 }, DataType::Float32, 0.0f, 0, true));
+    activationLayer->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 1, 4 }, DataType::Float32));
+
+    IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+
+    // Load it into the runtime. It should pass.
+    NetworkId netId;
+    std::string ignoredErrorMessage;
+
+    INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined);
+
+    CHECK(runtime->LoadNetwork(netId, std::move(optNet),ignoredErrorMessage, networkProperties)
+               == Status::Success);
+
+    // Creates structures for input & output
+    std::vector<float> inputData
+    {
+        1.0f, 2.0f, 3.0f, 4.0f
+    };
+
+    std::vector<float> outputData(4);
+
+    std::vector<float> expectedOutput
+    {
+         1.0f, 4.0f, 9.0f, 16.0f
+    };
+
+    InputTensors inputTensors
+    {
+        {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())},
+    };
+    OutputTensors outputTensors
+    {
+        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
+    };
+
+    runtime->GetProfiler(netId)->EnableProfiling(true);
+
+    std::vector<ImportedInputId> importedInputIds =
+        runtime->ImportInputs(netId, inputTensors, MemorySource::Malloc);
+    std::vector<ImportedOutputId> importedOutputIds =
+        runtime->ImportOutputs(netId, outputTensors, MemorySource::Malloc);
+
+    // Do the inference and force the import as the memory is alligned.
+    runtime->EnqueueWorkload(netId, inputTensors, outputTensors, importedInputIds, importedOutputIds);
+
+    // Retrieve the Profiler.Print() output to get the workload execution
+    ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance();
+    std::stringstream ss;
+    profilerManager.GetProfiler()->Print(ss);;
+    std::string dump = ss.str();
+
+    // Check there is a SyncMemGeneric workload as we exported
+    int count = SubStringCounter(dump, "SyncMemGeneric");
+    CHECK(count == 1);
+
+    // Shouldn't be any CopyMemGeneric workloads
+    count = SubStringCounter(dump, "CopyMemGeneric");
+    CHECK(count == 0);
+
+    // Check the output is correct
+    CHECK(std::equal(outputData.begin(), outputData.end(), expectedOutput.begin(), expectedOutput.end()));
+}
+
 #if !defined(__ANDROID__)
 // Only run these tests on non Android platforms
 TEST_CASE("RefImportNonAlignedPointerTest")