diff --git a/ArmnnPreparedModel_1_2.cpp b/ArmnnPreparedModel_1_2.cpp
index 5031c5f..76ef426 100644
--- a/ArmnnPreparedModel_1_2.cpp
+++ b/ArmnnPreparedModel_1_2.cpp
@@ -2,9 +2,6 @@
 // Copyright © 2017 Arm Ltd. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
-// Note: the ArmnnBurstExecutorWithCache in this file is based on Android code
-//       under the Apache 2.0 license. See comment below for details.
-//
 
 #define LOG_TAG "ArmnnDriver"
 
@@ -215,27 +212,6 @@
     return Execute(request, measureTiming, cb);
 }
 
-OutputShape ComputeShape(const armnn::TensorInfo& info)
-{
-    OutputShape shape;
-
-    hidl_vec<uint32_t> dimensions;
-
-    armnn::TensorShape tensorShape = info.GetShape();
-    const unsigned int numDims = tensorShape.GetNumDimensions();
-    dimensions.resize(numDims);
-
-    for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx)
-    {
-        dimensions[outputIdx] = tensorShape[outputIdx];
-    }
-
-    shape.dimensions = dimensions;
-    shape.isSufficient = true;
-
-    return shape;
-}
-
 template<typename HalVersion>
 Return<V1_0::ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::PrepareMemoryForInputs(
     armnn::InputTensors& inputs,
@@ -348,27 +324,6 @@
     return V1_0::ErrorStatus::NONE;
 }
 
-void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools)
-{
-    if (memPools.empty())
-    {
-        return;
-    }
-    // Commit output buffers.
-    // Note that we update *all* pools, even if they aren't actually used as outputs -
-    // this is simpler and is what the CpuExecutor does.
-    for (auto& pool : memPools)
-    {
-        // Type android::nn::RunTimePoolInfo has changed between Android P & Q and Android R, where
-        // update() has been removed and flush() added.
-#if defined(ARMNN_ANDROID_R) // Use the new Android implementation.
-        pool.flush();
-#else
-        pool.update();
-#endif
-    }
-}
-
 template<typename HalVersion>
 Return<void> ArmnnPreparedModel_1_2<HalVersion>::executeSynchronously(const V1_0::Request& request,
                                                                       MeasureTiming measureTiming,
@@ -514,7 +469,7 @@
 {
     std::vector<std::vector<char>> storage;
     armnn::InputTensors inputTensors;
-    for (unsigned int i = 0; i < m_Model.inputIndexes.size(); i++)
+    for (unsigned int i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
     {
         const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
         storage.emplace_back(inputTensorInfo.GetNumBytes());
@@ -524,7 +479,7 @@
     }
 
     armnn::OutputTensors outputTensors;
-    for (unsigned int i = 0; i < m_Model.outputIndexes.size(); i++)
+    for (unsigned int i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
     {
         const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
         storage.emplace_back(outputTensorInfo.GetNumBytes());
@@ -600,77 +555,6 @@
     return V1_0::ErrorStatus::NONE;
 }
 
-
-/// This class is strongly inspired by the default implementation in Android named DefaultBurstExecutorWithCache.
-/// The original code is licensed under Apache-2.0 and can be found at the following link:
-/// https://android.googlesource.com/platform/frameworks/
-///         ml/+/refs/tags/android-10.0.0_r20/nn/common/ExecutionBurstServer.cpp
-class ArmnnBurstExecutorWithCache : public ExecutionBurstServer::IBurstExecutorWithCache {
-public:
-    ArmnnBurstExecutorWithCache(V1_2::IPreparedModel* preparedModel)
-        : m_PreparedModel(preparedModel)
-    {}
-
-    bool isCacheEntryPresent(int32_t slot) const override
-    {
-        const auto it = m_MemoryCache.find(slot);
-        return (it != m_MemoryCache.end()) && it->second.valid();
-    }
-
-    void addCacheEntry(const hidl_memory& memory, int32_t slot) override
-    {
-        m_MemoryCache[slot] = memory;
-    }
-
-    void removeCacheEntry(int32_t slot) override
-    {
-        m_MemoryCache.erase(slot);
-    }
-
-    std::tuple<V1_0::ErrorStatus, hidl_vec<OutputShape>, Timing> execute(
-        const V1_0::Request& request, const std::vector<int32_t>& slots,
-        MeasureTiming measure) override
-    {
-        ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache::execute");
-        hidl_vec<hidl_memory> pools(slots.size());
-
-        std::transform(slots.begin(), slots.end(), pools.begin(), [this](int32_t slot)
-            {
-                return m_MemoryCache[slot];
-            });
-
-        V1_0::Request fullRequest = request;
-        fullRequest.pools = std::move(pools);
-
-        // Setup Callback
-        V1_0::ErrorStatus returnedStatus = V1_0::ErrorStatus::GENERAL_FAILURE;
-        hidl_vec<OutputShape> returnedOutputShapes;
-        Timing returnedTiming;
-        auto cb = [&returnedStatus, &returnedOutputShapes, &returnedTiming](V1_0::ErrorStatus status,
-                                                                            const hidl_vec<OutputShape>& outputShapes,
-                                                                            const Timing& timing)
-            {
-                returnedStatus = status;
-                returnedOutputShapes = outputShapes;
-                returnedTiming = timing;
-            };
-
-        // Execute
-        ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache executing");
-        const Return<void> ret = m_PreparedModel->executeSynchronously(fullRequest, measure, cb);
-
-        if (!ret.isOk() || returnedStatus != V1_0::ErrorStatus::NONE)
-        {
-            ALOGE("ArmnnPreparedModel_1_2::BurstExecutorWithCache::error executing");
-        }
-        return std::make_tuple(returnedStatus, std::move(returnedOutputShapes), returnedTiming);
-    }
-
-private:
-    V1_2::IPreparedModel* const m_PreparedModel;
-    std::map<int, hidl_memory> m_MemoryCache;
-};
-
 template<typename HalVersion>
 Return<void> ArmnnPreparedModel_1_2<HalVersion>::configureExecutionBurst(
     const sp<V1_2::IBurstCallback>& callback,
@@ -679,12 +563,10 @@
     V1_2::IPreparedModel::configureExecutionBurst_cb cb)
 {
     ALOGV("ArmnnPreparedModel_1_2::configureExecutionBurst");
-    const std::shared_ptr<ArmnnBurstExecutorWithCache> executorWithCache =
-        std::make_shared<ArmnnBurstExecutorWithCache>(this);
     const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback,
                                                                        requestChannel,
                                                                        resultChannel,
-                                                                       executorWithCache);
+                                                                       this);
 
     if (burst == nullptr)
     {
@@ -697,9 +579,7 @@
     return Void();
 }
 
-
-
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
 template class ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>;
 template bool ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>::ExecuteGraph<CallbackContext_1_2>(
         std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,