diff --git a/scripts/cmake/tensorflow.cmake b/scripts/cmake/tensorflow.cmake
index d807416..29d5bce 100644
--- a/scripts/cmake/tensorflow.cmake
+++ b/scripts/cmake/tensorflow.cmake
@@ -71,7 +71,16 @@
 endif()
 
 if (TENSORFLOW_LITE_MICRO_CLEAN_DOWNLOADS)
-    list(APPEND MAKE_TARGETS_LIST "clean_downloads")
+    message(STATUS "Refreshing TensorFlow Lite Micro's third party downloads...")
+    execute_process(
+        COMMAND make -f ${TENSORFLOW_LITE_MICRO_PATH}/tools/make/Makefile clean_downloads third_party_downloads
+        RESULT_VARIABLE return_code
+        WORKING_DIRECTORY ${TENSORFLOW_SRC_PATH})
+    if (NOT return_code EQUAL "0")
+        message(FATAL_ERROR "Failed to clean TensorFlow Lite Micro's third party downloads.")
+    else()
+        message(STATUS "Refresh completed.")
+    endif ()
 endif()
 
 if (TENSORFLOW_LITE_MICRO_CLEAN_BUILD)
diff --git a/scripts/py/setup_hooks.py b/scripts/py/setup_hooks.py
index 97a2861..178765c 100644
--- a/scripts/py/setup_hooks.py
+++ b/scripts/py/setup_hooks.py
@@ -55,25 +55,25 @@
 while read local_ref local_sha remote_ref remote_sha
 do
     # We should pass only added or modified C/C++ source files to cppcheck.
-    changed_files=$(git diff --name-only HEAD~1 HEAD | grep -E "*\.(c|cpp|cc|cxx)" | cut -f 2)
-                if [ -n "$changed_files" ]; then
-                                    clang-format -style=file --dry-run --Werror $changed_files
+    changed_files=$(git diff --name-only HEAD~1 HEAD | grep -iE "\.(c|cpp|cxx|cc|h|hpp|hxx)$" | cut -f 2)
+        if [ -n "$changed_files" ]; then
+            clang-format -style=file --dry-run --Werror $changed_files
 
-                                    exitcode1=$?
-                                    if [ $exitcode1 -ne 0 ]; then
-                                        echo "Formatting errors found in file: $changed_files.
-                                        \nPlease run:\n\    \"clang-format -style=file -i $changed_files\"
-                                        \nto correct these errors"
-                                        exit $exitcode1
-                                    fi
+            exitcode1=$?
+            if [ $exitcode1 -ne 0 ]; then
+                echo "Formatting errors found in file: $changed_files.
+                \nPlease run:\n\    \"clang-format -style=file -i $changed_files\"
+                \nto correct these errors"
+                exit $exitcode1
+            fi
 
-                    cppcheck --enable=performance,portability  --error-exitcode=1 $changed_files
-                                    exitcode2=$?
-                                    if [ $exitcode2 -ne 0 ]; then
-                                        exit $exitcode2
-                                    fi
-                fi
-            exit 0
+            cppcheck --enable=performance,portability  --error-exitcode=1 $changed_files
+            exitcode2=$?
+            if [ $exitcode2 -ne 0 ]; then
+                exit $exitcode2
+            fi
+        fi
+    exit 0
 done
 
 exit 0'''
diff --git a/source/application/api/common/source/Model.cc b/source/application/api/common/source/Model.cc
index 8467d71..7de6fde 100644
--- a/source/application/api/common/source/Model.cc
+++ b/source/application/api/common/source/Model.cc
@@ -1,6 +1,6 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,16 +22,13 @@
 /* Initialise the model */
 arm::app::Model::~Model()
 {
-   delete this->m_pInterpreter;
+    delete this->m_pInterpreter;
     /**
      * No clean-up function available for allocator in TensorFlow Lite Micro yet.
      **/
 }
 
-arm::app::Model::Model() :
-    m_inited (false),
-    m_type(kTfLiteNoType)
-{}
+arm::app::Model::Model() : m_inited(false), m_type(kTfLiteNoType) {}
 
 bool arm::app::Model::Init(uint8_t* tensorArenaAddr,
                            uint32_t tensorArenaSize,
@@ -48,10 +45,10 @@
     this->m_pModel = ::tflite::GetModel(nnModelAddr);
 
     if (this->m_pModel->version() != TFLITE_SCHEMA_VERSION) {
-        printf_err(
-            "Model's schema version %d is not equal "
-            "to supported version %d.",
-            this->m_pModel->version(), TFLITE_SCHEMA_VERSION);
+        printf_err("Model's schema version %" PRIu32 " is not equal "
+                   "to supported version %d.",
+                   this->m_pModel->version(),
+                   TFLITE_SCHEMA_VERSION);
         return false;
     }
 
@@ -75,9 +72,7 @@
         /* Create an allocator instance */
         info("Creating allocator using tensor arena at 0x%p\n", tensorArenaAddr);
 
-        this->m_pAllocator = tflite::MicroAllocator::Create(
-                                        tensorArenaAddr,
-                                        tensorArenaSize);
+        this->m_pAllocator = tflite::MicroAllocator::Create(tensorArenaAddr, tensorArenaSize);
 
         if (!this->m_pAllocator) {
             printf_err("Failed to create allocator\n");
@@ -88,9 +83,8 @@
         debug("Using existing allocator @ 0x%p\n", this->m_pAllocator);
     }
 
-    this->m_pInterpreter = new ::tflite::MicroInterpreter(
-        this->m_pModel, this->GetOpResolver(),
-        this->m_pAllocator);
+    this->m_pInterpreter =
+        new ::tflite::MicroInterpreter(this->m_pModel, this->GetOpResolver(), this->m_pAllocator);
 
     if (!this->m_pInterpreter) {
         printf_err("Failed to allocate interpreter\n");
@@ -120,7 +114,7 @@
         printf_err("failed to get tensors\n");
         return false;
     } else {
-        this->m_type = this->m_input[0]->type;  /* Input 0 should be the main input */
+        this->m_type = this->m_input[0]->type; /* Input 0 should be the main input */
 
         /* Clear the input & output tensors */
         for (size_t inIndex = 0; inIndex < this->GetNumInputs(); inIndex++) {
@@ -155,10 +149,9 @@
 
     debug("\ttensor is assigned to 0x%p\n", tensor);
     info("\ttensor type is %s\n", TfLiteTypeGetName(tensor->type));
-    info("\ttensor occupies %zu bytes with dimensions\n",
-         tensor->bytes);
-    for (int i = 0 ; i < tensor->dims->size; ++i) {
-        info ("\t\t%d: %3d\n", i, tensor->dims->data[i]);
+    info("\ttensor occupies %zu bytes with dimensions\n", tensor->bytes);
+    for (int i = 0; i < tensor->dims->size; ++i) {
+        info("\t\t%d: %3d\n", i, tensor->dims->data[i]);
     }
 
     TfLiteQuantization quant = tensor->quantization;
@@ -192,7 +185,7 @@
     }
 
     info("Activation buffer (a.k.a tensor arena) size used: %zu\n",
-        this->m_pInterpreter->arena_used_bytes());
+         this->m_pInterpreter->arena_used_bytes());
 
     /* We expect there to be only one subgraph. */
     const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0);
@@ -203,10 +196,10 @@
     auto* opcodes = this->m_pModel->operator_codes();
 
     /* For each operator, display registration information. */
-    for (size_t i = 0 ; i < nOperators; ++i) {
-        const tflite::Operator* op = subgraph->operators()->Get(i);
+    for (size_t i = 0; i < nOperators; ++i) {
+        const tflite::Operator* op         = subgraph->operators()->Get(i);
         const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index());
-        const TfLiteRegistration* reg = nullptr;
+        const TfLiteRegistration* reg      = nullptr;
 
         tflite::GetRegistrationFromOpCode(opcode, this->GetOpResolver(), &reg);
         std::string opName;
@@ -215,8 +208,8 @@
             if (tflite::BuiltinOperator_CUSTOM == reg->builtin_code) {
                 opName = std::string(reg->custom_name);
             } else {
-                opName = std::string(EnumNameBuiltinOperator(
-                            tflite::BuiltinOperator(reg->builtin_code)));
+                opName = std::string(
+                    EnumNameBuiltinOperator(tflite::BuiltinOperator(reg->builtin_code)));
             }
         }
         info("\tOperator %zu: %s\n", i, opName.c_str());
@@ -236,21 +229,19 @@
 bool arm::app::Model::ContainsEthosUOperator() const
 {
     /* We expect there to be only one subgraph. */
-    const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0);
+    const uint32_t nOperators        = tflite::NumSubgraphOperators(this->m_pModel, 0);
     const tflite::SubGraph* subgraph = this->m_pModel->subgraphs()->Get(0);
-    const auto* opcodes = this->m_pModel->operator_codes();
+    const auto* opcodes              = this->m_pModel->operator_codes();
 
     /* check for custom operators */
-    for (size_t i = 0; (i < nOperators); ++i)
-    {
-        const tflite::Operator* op = subgraph->operators()->Get(i);
+    for (size_t i = 0; (i < nOperators); ++i) {
+        const tflite::Operator* op         = subgraph->operators()->Get(i);
         const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index());
 
         auto builtin_code = tflite::GetBuiltinCode(opcode);
         if ((builtin_code == tflite::BuiltinOperator_CUSTOM) &&
-            ( nullptr != opcode->custom_code()) &&
-            ( "ethos-u" == std::string(opcode->custom_code()->c_str())))
-        {
+            (nullptr != opcode->custom_code()) &&
+            ("ethos-u" == std::string(opcode->custom_code()->c_str()))) {
             return true;
         }
     }
@@ -304,7 +295,6 @@
     return 0;
 }
 
-
 TfLiteType arm::app::Model::GetType() const
 {
     return this->m_type;
@@ -339,7 +329,8 @@
     info("Model info:\n");
     this->LogInterpreterInfo();
 
-    info("The model is optimised for Ethos-U NPU: %s.\n", this->ContainsEthosUOperator()? "yes": "no");
+    info("The model is optimised for Ethos-U NPU: %s.\n",
+         this->ContainsEthosUOperator() ? "yes" : "no");
 
     return true;
 }