Building the ML embedded code sample applications from sources

This section assumes that you are using an x86 Linux build machine.

Build prerequisites

Before proceeding, it is essential to ensure that the following prerequisites have been fulfilled:

  • GNU Arm embedded toolchain 10.2.1 (or higher) or the Arm Compiler version 6.14, or higher, is installed and available on the path. Test the compiler by running:

    armclang -v
    Product: ARM Compiler 6.14 Professional
    Component: ARM Compiler 6.14

    Alternatively, use:

    arm-none-eabi-gcc --version
    arm-none-eabi-gcc (GNU Arm Embedded Toolchain 10-2020-q4-major) 10.2.1 20201103 (release)
    Copyright (C) 2020 Free Software Foundation, Inc.
    This is free software; see the source for copying conditions.  There is NO

Note: If required, add the compiler to the path:

export PATH=/path/to/armclang/bin:$PATH OR export PATH=/path/to/gcc-arm-none-eabi-toolchain/bin:$PATH

  • If you are using the proprietary Arm Compiler, ensure that the compiler license has been correctly configured.

  • CMake version 3.15 or above is installed and available on the path. Test CMake by running:

    cmake --version
    cmake version 3.16.2

Note: How to add cmake to the path:

export PATH=/path/to/cmake/bin:$PATH

  • Python 3.6 or above is installed. Check your current installed version of Python by running:

    python3 --version
    Python 3.6.8
  • The build system creates a Python virtual environment during the build process. Please make sure that Python virtual environment module is installed by running:

    python3 -m venv
  • Make

    make --version
    GNU Make 4.1

Note: Add it to the path environment variable, if needed.

  • Access to the internet to download the third-party dependencies, specifically: TensorFlow Lite Micro, Arm® Ethos™-U55 NPU driver, and CMSIS. Instructions for downloading these are listed under: preparing build environment.

Build options

The project build system allows you to specify custom neural network models (in the .tflite format) for each use-case along with the network inputs.

It also builds TensorFlow Lite for Microcontrollers library, Arm® Ethos™-U55 driver library, and the CMSIS-DSP library from sources.

The build script is parameterized to support different options. Default values for build parameters build the applications for all use-cases where the Arm® Corstone™-300 design can execute on an MPS3 FPGA or the Fixed Virtual Platform (FVP).

The build parameters are:

  • TARGET_PLATFORM: The target platform to execute the application on:

    • mps3 (default)
    • native
    • simple_platform
  • TARGET_SUBSYSTEM: The target platform subsystem. Specifies the design implementation for the deployment target. For both, the MPS3 FVP and the MPS3 FPGA, this must be left to the default value of SSE-300:

  • CMAKE_TOOLCHAIN_FILE: This built-in CMake parameter can be used to override the default toolchain file used for the build. All the valid toolchain files are located in the scripts directory. For example, see: bare-metal-gcc.cmake.

  • TENSORFLOW_SRC_PATH: the path to the root of the TensorFlow directory. The default value points to the dependencies/tensorflow git submodule. Respository is hosted here: tensorflow

  • ETHOS_U55_DRIVER_SRC_PATH: The path to the Ethos-U55 NPU core driver sources. The default value points to the dependencies/core-driver git submodule. Repository is hosted here: ethos-u-core-driver.

  • CMSIS_SRC_PATH: The path to the CMSIS sources to be used to build TensorFlow Lite Micro library. This parameter is optional and is only valid for Arm® Cortex®-M CPU targeted configurations. The default value points to the dependencies/cmsis git submodule. Respository is hosted here: CMSIS-5

  • ETHOS_U55_ENABLED: Sets whether the use of Ethos-U55 NPU is available for the deployment target. By default, this is set and therefore application is built with Ethos-U55 NPU supported.

  • CPU_PROFILE_ENABLED: Sets whether profiling information for the CPU core should be displayed. By default, this is set to false, but can be turned on for FPGA targets. The the FVP and the CPU core cycle counts are not meaningful and are not to be used.

  • LOG_LEVEL: Sets the verbosity level for the output of the application over UART, or stdout. Valid values are: LOG_LEVEL_TRACE, LOG_LEVEL_DEBUG, LOG_LEVEL_INFO, LOG_LEVEL_WARN, and LOG_LEVEL_ERROR. The default is set to: LOG_LEVEL_INFO.

  • <use_case>_MODEL_TFLITE_PATH: The path to the model file that is processed and is included into the application axf file. The default value points to one of the delivered set of models. Make sure that the model chosen is aligned with the ETHOS_U55_ENABLED setting.

    • When using the Ethos-U55 NPU backend, the NN model is assumed to be optimized by Vela compiler. However, even if not, if it is supported by TensorFlow Lite Micro, it falls back on the CPU and execute.

    • When use of the Ethos-U55 NPU is disabled, and if a Vela optimized model is provided, then the application reports a failure at runtime.

  • USE_CASE_BUILD: Specifies the list of applications to build. By default, the build system scans sources to identify available ML applications and produces executables for all detected use-cases. This parameter can accept single value, for example: USE_CASE_BUILD=img_class, or multiple values. For example: USE_CASE_BUILD="img_class;kws".

  • ETHOS_U55_TIMING_ADAPTER_SRC_PATH: The path to timing adapter sources. The default value points to the timing_adapter dependencies folder.

  • TA_CONFIG_FILE: The path to the CMake configuration file that contains the timing adapter parameters. Used only if the timing adapter build is enabled.

  • TENSORFLOW_LITE_MICRO_CLEAN_BUILD: Optional parameter to enable, or disable, "cleaning" prior to building for the TensorFlow Lite Micro library. Enabled by default.

  • TENSORFLOW_LITE_MICRO_CLEAN_DOWNLOADS: Optional parameter to enable wiping out TPIP downloads from TensorFlow source tree prior to each build. Disabled by default.

  • ARMCLANG_DEBUG_DWARF_LEVEL: When the CMake build type is specified as Debug and when the armclang toolchain is being used to build for a Cortex-M CPU target, this optional argument can be set to specify the DWARF format.

    By default, this is set to 4 and is synonymous with passing -g flag to the compiler. This is compatible with Arm DS and other tools which can interpret the latest DWARF format. To allow debugging using the Model Debugger from Arm Fast Model Tools Suite, this argument can be used to pass DWARF format version as "3".

    Note: This option is only available when the CMake project is configured with the -DCMAKE_BUILD_TYPE=Debug argument. Also, the same dwarf format is used for building TensorFlow Lite Micro library.

For details on the specific use-case build options, follow the instructions in the use-case specific documentation.

Also, when setting any of the CMake configuration parameters that expect a directory, or file, path, use absolute paths instead of relative paths.

Build process

The build process uses three major steps:

  1. Prepare the build environment by downloading third-party sources required, see Preparing build environment.

  2. Configure the build for the platform chosen. This stage includes:

    • CMake options configuration
    • When <use_case>_MODEL_TFLITE_PATH build options are not provided, the default neural network models can be downloaded from Arm ML-Zoo. For native builds, the network input and output data for tests are downloaded.
    • Some files such as neural network models, network inputs, and output labels are automatically converted into C/C++ arrays, see: Automatic file generation.
  3. Build the application.
    Application and third-party libraries are now built. For further information, see: Building the configured project.

Preparing build environment

Fetching submodules

Certain third-party sources are required to be present on the development machine to allow the example sources in this repository to link against.

  1. TensorFlow Lite Micro repository
  2. Ethos-U55 NPU core driver repository
  3. CMSIS-5

Note: If you are using non git project sources, run python3 ./ and ignore further git instructions. Proceed to Fetching resource files section.

To pull the submodules:

git submodule update --init

This downloads all of the required components and places them in a tree, like so:

    ├── cmsis
    ├── core-driver
    ├── core-software
    └── tensorflow

Note: The default source paths for the TPIP sources assume the above directory structure. However, all of the relevant paths can be overridden by CMake configuration arguments TENSORFLOW_SRC_PATH ETHOS_U55_DRIVER_SRC_PATH, and CMSIS_SRC_PATH.

Fetching resource files

Every ML use-case example in this repository also depends on external neural network models. To download these, run the following command from the root of the repository:

python3 ./

This fetches every model into the resources_downloaded directory. It also optimizes the models using the Vela compiler for the default 128 MAC configuration of the Arm® Ethos™-U55 NPU.

Note: This script requires Python version 3.6 or higher. Please make sure all build prerequisites are satisfied.

Building for default configuration

A helper script is provided to configure and build all the applications. It configures the project with default settings i.e., for mps3 target and sse-300 subsystem. Under the hood, it invokes all the necessary CMake commands that are described in the next sections.

If using the Arm GNU embedded toolchain, execute:


If using the Arm Compiler, execute:

./ --toolchain arm

Additional command line arguments supported by this script are:

  • --skip-download: Do not download resources: models and test vectors
  • --skip-vela: Do not run Vela optimizer on downloaded models.

Create a build directory

To configure the build project manually, create a build directory in the root of the project and navigate inside:

mkdir build && cd build

Configuring the build for MPS3 SSE-300

Using GNU Arm Embedded toolchain

On Linux, if using Arm GNU embedded toolchain, execute the following command to build the application to run on the Arm® Ethos™-U55 NPU when providing only the mandatory arguments for CMake configuration:

cmake ../

The preceding command builds for the default target platform mps3, the default subsystem sse-300, and using the default toolchain file for the target as bare-metal-gcc. This is equivalent to running:

cmake .. \

Using Arm Compiler

If using Arm Compiler to set the compiler and platform-specific parameters, the toolchain option CMAKE_TOOLCHAIN_FILE can be used to point to the ARMClang CMake file, like so:

cmake ../ -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake

To configure a build that can be debugged using Arm Development Studio, specify the build type as Debug. For example:

cmake .. \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \

Generating project for Arm Development Studio

To import the project into Arm Development Studio, add the Eclipse project generator and CMAKE_ECLIPSE_VERSION in the CMake command.

It is advisable that the build directory is one level up relative to the source directory. When the build has been generated, you must follow the Import wizard in Arm Development Studio and import the existing project into the workspace.

You can then compile and debug the project using Arm Development Studio. Note that the following command is executed one level up from the source directory:

cmake \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \
    -G "Eclipse CDT4 - Unix Makefiles" \

Working with model debugger from Arm Fast Model Tools

To configure a build that can be debugged using a tool that only supports the DWARF format 3, such as Modeldebugger, you can use:

cmake .. \
    -DTARGET_SUBSYSTEM=sse-300 \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \

Configuring with custom TPIP dependencies

If the TensorFlow source tree is not in its default expected location, set the path using TENSORFLOW_SRC_PATH. Similarly, if the Ethos-U55 NPU driver and CMSIS are not in the default location, then use ETHOS_U55_DRIVER_SRC_PATH and CMSIS_SRC_PATH to configure their location.

For example:

cmake .. \
    -DTENSORFLOW_SRC_PATH=/my/custom/location/tensorflow \
    -DETHOS_U55_DRIVER_SRC_PATH=/my/custom/location/core-driver \

Note: If re-building with changed parameters values, we recommend that you clean the build directory and re-run the CMake command.

Configuring native unit-test build

cmake ../ -DTARGET_PLATFORM=native

Results of the build are placed under the build/bin/ folder. For example:

├── arm_ml_embedded_evaluation_kit-<usecase1>-tests
├── arm_ml_embedded_evaluation_kit-<usecase2>-tests
├── ethos-u-<usecase1>
└── ethos-u-<usecase1>

Configuring the build for simple_platform

cmake ../ -DTARGET_PLATFORM=simple_platform

Again, if using Arm Compiler, use:

cmake .. \
    -DTARGET_PLATFORM=simple_platform \

Building the configured project

If the CMake command succeeds, build the application as follows:

make -j4

To see compilation and link details, add VERBOSE=1.

Results of the build are placed under build/bin folder, for example:

 ├── ethos-u-<use_case_name>.axf
 ├── ethos-u-<use_case_name>.htm
 ├── ethos-u-<use_case_name>.map
 └── sectors
        ├── images.txt
        └── <use_case>
                ├── ddr.bin
                └── itcm.bin

Where for each implemented use-case under the source/use-case directory, the following build artifacts are created:

  • ethos-u-<use-case name>.axf: The built application binary for an ML use-case.

  • ethos-u-<use-case name>.map: Information from building the application. For example: Libraries used, what was optimized, and location of objects).

  • ethos-u-<use-case name>.htm: Human readable file containing the call graph of application functions.

  • sectors/<use-case>: Folder containing the built application. Split into files for loading into different FPGA memory regions.

  • images.txt: Tells the FPGA which memory regions to use for loading the binaries in the sectors/.. folder.

Note: For the specific use-case commands, refer to the relative section in the use-case documentation.

Building timing adapter with custom options

The sources also contain the configuration for a timing adapter utility for the Ethos-U55 NPU driver. The timing adapter allows the platform to simulate user provided memory bandwidth and latency constraints.

The timing adapter driver aims to control the behavior of two AXI buses used by Ethos-U55 NPU. One is for SRAM memory region, and the other is for flash or DRAM.

The SRAM is where intermediate buffers are expected to be allocated and therefore, this region can serve frequent Read and Write traffic generated by computation operations while executing a neural network inference.

The flash or DDR is where we expect to store the model weights and therefore, this bus would only usually be used for RO traffic.

It is used for MPS3 FPGA and for Fast Model environment.

The CMake build framework allows the parameters to control the behavior of each bus with following parameters:

  • MAXR: Maximum number of pending read operations allowed. 0 is inferred as infinite and the default value is 4.

  • MAXW: Maximum number of pending write operations allowed. 0 is inferred as infinite and the default value is 4.

  • MAXRW: Maximum number of pending read and write operations allowed. 0 is inferred as infinite and the default value is 8.

  • RLATENCY: Minimum latency, in cycle counts, for a read operation. This is the duration between ARVALID and RVALID signals. The default value is 50.

  • WLATENCY: Minimum latency, in cycle counts, for a write operation. This is the duration between WVALID and WLAST, with BVALID being deasserted. The default value is 50.

  • PULSE_ON: The number of cycles where addresses are let through. The default value is 5100.

  • PULSE_OFF: The number of cycles where addresses are blocked. The default value is 5100.

  • BWCAP: Maximum number of 64-bit words transferred per pulse cycle. A pulse cycle is PULSE_ON and PULSE_OFF. 0 is inferred as infinite and the default value is 625.

  • MODE: Timing adapter operation mode. Default value is 0.

    • Bit 0: 0=simple, 1=latency-deadline QoS throttling of read versus write,

    • Bit 1: 1=enable random AR reordering (0=default),

    • Bit 2: 1=enable random R reordering (0=default),

    • Bit 3: 1=enable random B reordering (0=default)

For the CMake build configuration of the timing adapter, the SRAM AXI is assigned index 0 and the flash, or DRAM, AXI bus has index 1.

To change the bus parameter for the build a "TA_<index>_" prefix should be added to the above. For example, TA0_MAXR=10 sets the maximum pending reads to 10 on the SRAM AXI bus.

As an example, if we have the following parameters for the flash, or DRAM, region:

  • TA1_MAXR = "2"

  • TA1_MAXW = "0"

  • TA1_MAXRW = "0"

  • TA1_RLATENCY = "64"

  • TA1_WLATENCY = "32"

  • TA1_PULSE_ON = "320"

  • TA1_PULSE_OFF = "80"

  • TA1_BWCAP = "50"

For a clock rate of 500MHz, this would translate to:

  • The maximum duty cycle for any operation is:
    Maximum duty cycle formula

  • Maximum bit rate for this bus (64-bit wide) is:
    Maximum bit rate formula

  • With a read latency of 64 cycles, and maximum pending reads as 2, each read could be a maximum of 64 or 128 bytes. As defined for the Ethos-U55 NPU AXI bus attribute.

    The bandwidth is calculated solely by read parameters:

    Bandwidth formula

    This is higher than the overall bandwidth dictated by the bus parameters of:

    Overall bandwidth formula

This suggests that the read operation is only limited by the overall bus bandwidth.

Timing adapter requires recompilation to change parameters. Default timing adapter configuration file pointed to by TA_CONFIG_FILE build parameter is located in the scripts/cmake folder and contains all options for AXI0 and AXI1 as previously described.

here is an example of scripts/cmake/ta_config.cmake:

# Timing adapter options

# Timing adapter settings for AXI0
set(TA0_MAXR "8")
set(TA0_MAXW "8")
set(TA0_MAXRW "0")
set(TA0_RLATENCY "32")
set(TA0_WLATENCY "32")
set(TA0_PULSE_ON "3999")
set(TA0_PULSE_OFF "1")
set(TA0_BWCAP "4000")

An example of the build with a custom timing adapter configuration:

cmake .. -DTA_CONFIG_FILE=scripts/cmake/my_ta_config.cmake

Add custom inputs

The application performs inference on input data found in the folder set by the CMake parameters, for more information see section 3.3 in the specific use-case documentation.

Add custom model

The application performs inference using the model pointed to by the CMake parameter MODEL_TFLITE_PATH.

Note: If you want to run the model using Ethos-U55 NPU, ensure that your custom model has been run through the Vela compiler successfully before continuing.

To run the application with a custom model, you must provide a labels_<model_name>.txt file of labels that are associated with the model.

Each line of the file should correspond to one of the outputs in your model. See the provided labels_mobilenet_v2_1.0_224.txt file in the img_class use-case for an example.

Then, you must set <use_case>_MODEL_TFLITE_PATH to the location of the Vela processed model file and <use_case>_LABELS_TXT_FILE to the location of the associated labels file, like so:

cmake .. \
    -D<use_case>_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \
    -D<use_case>_LABELS_TXT_FILE=<path/to/labels_custom_model.txt> \
    -DTARGET_SUBSYSTEM=sse-300 \

Note: For the specific use-case command, refer to the relative section in the use-case documentation.

Note: Clean the build directory before re-running the CMake command.

The TensorFlow Lite for Microcontrollers model pointed to by <use_case>_MODEL_TFLITE_PATH and the labels text file pointed to by <use_case>_LABELS_TXT_FILE are converted to C++ files during the CMake configuration stage. They are then compiled into the application for performing inference with.

The log from the configuration stage tells you what model path and labels file have been used. For example:

-- User option TARGET_PLATFORM is set to mps3
-- User option <use_case>_MODEL_TFLITE_PATH is set to
-- User option <use_case>_LABELS_TXT_FILE is set to
-- Using <path/to/custom_model_after_vela.tflite>
++ Converting custom_model_after_vela.tflite to
-- Generating labels file from <path/to/labels_custom_model.txt>
-- writing to <path/to/build>/generated/include/Labels.hpp and <path/to/build>/generated/src/

After compiling, your custom model has now replaced the default one in the application.

Optimize custom model with Vela compiler

Note: This tool is not available within this project. It is a Python tool available from
The source code is hosted on

The Vela compiler is a tool that can optimize a neural network model into a version that can run on an embedded system containing an Ethos-U55 NPU.

The optimized model contains custom operators for sub-graphs of the model that can be accelerated by the Ethos-U55 NPU. The remaining layers that cannot be accelerated, are left unchanged and are run on the CPU using optimized, or CMSIS-NN, or reference kernels that are provided by the inference engine.

After the compilation, the optimized model can only be executed on a system using an Ethos-U55 NPU.

Note: The NN model provided during the build and compiled into the application executable binary defines whether the CPU or NPU is used to execute workloads. If an unoptimized model is used, then inference runs on the Cortex-M CPU.

The Vela compiler accepts parameters to influence a model optimization. The model provided within this project has been optimized with the following parameters:

vela \
    --accelerator-config=ethos-u55-128 \
    --optimise Performance \
    --config my_vela_cfg.ini \
    --memory-mode Shared_Sram \
    --system-config Ethos_U55_High_End_Embedded \

The Vela command contains the following:

  • --accelerator-config: Specifies the accelerator configuration to use between ethos-u55-256, ethos-u55-128, ethos-u55-64, and ethos-u55-32.
  • --optimise: Sets the optimisation strategy to Performance or Size. The Size strategy results in a model minimising the SRAM usage whereas the Performance strategy optimises the neural network for maximal perforamance. Note that if using the Performance strategy, you can also pass the --arena-cache-size option to Vela.
  • --config: Specifies the path to the Vela configuration file. The format of the file is a Python ConfigParser .ini file. An example can be found in the dependencies folder default_vela.ini.
  • --memory-mode: Selects the memory mode to use as specified in the Vela configuration file.
  • --system-config: Selects the system configuration to use as specified in the Vela configuration file.

Vela compiler accepts .tflite file as input and saves optimized network model as a .tflite file.

Using --show-cpu-operations and --show-subgraph-io-summary shows all the operations that fall back to the CPU. And includes a summary of all the subgraphs and their inputs and outputs.

To see Vela helper for all the parameters use: vela --help.

Note: By default, use of the Ethos-U55 NPU is enabled in the CMake configuration. This can be changed by passing -DETHOS_U55_ENABLED.

Automatic file generation

As mentioned in the previous sections, some files such as neural network models, network inputs, and output labels are automatically converted into C/C++ arrays during the CMake project configuration stage.

Also, some code is generated to allow access to these arrays.

For example:

-- Building use-cases: img_class.
-- Found sources for use-case img_class
-- User option img_class_FILE_PATH is set to /tmp/samples
-- User option img_class_IMAGE_SIZE is set to 224
-- User option img_class_LABELS_TXT_FILE is set to /tmp/labels/labels_model.txt
-- Generating image files from /tmp/samples
++ Converting cat.bmp to
++ Converting dog.bmp to
-- Skipping file /tmp/samples/ due to unsupported image format.
++ Converting kimono.bmp to
++ Converting tiger.bmp to
++ Generating /tmp/build/generated/img_class/include/InputFiles.hpp
-- Generating labels file from /tmp/labels/labels_model.txt
-- writing to /tmp/build/generated/img_class/include/Labels.hpp and /tmp/build/generated/img_class/src/
-- User option img_class_ACTIVATION_BUF_SZ is set to 0x00200000
-- User option img_class_MODEL_TFLITE_PATH is set to /tmp/models/model.tflite
-- Using /tmp/models/model.tflite
++ Converting model.tflite to

In particular, the building options pointing to the input files <use_case>_FILE_PATH, the model <use_case>_MODEL_TFLITE_PATH, and labels text file <use_case>_LABELS_TXT_FILE are used by Python scripts in order to generate not only the converted array files, but also some headers with utility functions.

For example, the generated utility functions for image classification are:

  • build/generated/include/InputFiles.hpp

    #include <cstdint>
    #define NUMBER_OF_FILES  (2U)
    #define IMAGE_DATA_SIZE  (150528U)
    extern const uint8_t im0[IMAGE_DATA_SIZE];
    extern const uint8_t im1[IMAGE_DATA_SIZE];
    const char* get_filename(const uint32_t idx);
    const uint8_t* get_img_array(const uint32_t idx);
    #endif /* GENERATED_IMAGES_H */
  • build/generated/src/

    #include "InputFiles.hpp"
    static const char *img_filenames[] = {
    static const uint8_t *img_arrays[] = {
    const char* get_filename(const uint32_t idx)
        if (idx < NUMBER_OF_FILES) {
            return img_filenames[idx];
        return nullptr;
    const uint8_t* get_img_array(const uint32_t idx)
        if (idx < NUMBER_OF_FILES) {
            return img_arrays[idx];
        return nullptr;

These headers are generated using Python templates, that are located in scripts/py/templates/*.template:

├── cmake
   ├── ...
   ├── subsystem-profiles
      └── corstone-sse-300.cmake
   ├── templates
      ├── mem_regions.h.template
      ├── peripheral_irqs.h.template
      └── peripheral_memmap.h.template
   └── ...
└── py
    ├── <generation scripts>
    ├── requirements.txt
    └── templates
        ├── AudioClips.hpp.template
        ├── default.hpp.template
        ├── header_template.txt
        ├── Images.hpp.template
        ├── Labels.hpp.template
        ├── TestData.hpp.template

Based on the type of use-case, the correct conversion is called in the use-case cmake file. Or, audio or image respectively, for voice, or vision use-cases.

For example, the generations call for image classification, source/use_case/img_class/usecase.cmake, looks like:

# Generate input files

# Generate labels file
set(${use_case}_LABELS_CPP_FILE Labels)
    INPUT           "${${use_case}_LABELS_TXT_FILE}"


# Generate model file

Note: When required, for models and labels conversion, it is possible to add extra parameters such as extra code to put in <model>.cc file or namespaces.

set(${use_case}_LABELS_CPP_FILE Labels)
    INPUT           "${${use_case}_LABELS_TXT_FILE}"
    NAMESPACE       "namespace1" "namespace2"


    "/* Model parameters for ${use_case} */"
    "extern const int   g_myvariable2     = value1"
    "extern const int   g_myvariable2     = value2"

    NAMESPACE   "namespace1" "namespace2"

In addition to input file conversions, the correct platform, or system, profile is selected, in scripts/cmake/subsystem-profiles/*.cmake. It is based on TARGET_SUBSYSTEM build option and the variables set are used to generate memory region sizes, base addresses and IRQ numbers, respectively used to generate the mem_region.h, peripheral_irqs.h, and peripheral_memmap.h headers.

Templates from scripts/cmake/templates/*.template are used to generate the header files.

After the build, the files generated in the build folder are:

├── bsp
   ├── mem_regions.h
   ├── peripheral_irqs.h
   └── peripheral_memmap.h
├── <use_case_name1>
   ├── include
      ├── InputFiles.hpp
      └── Labels.hpp
   └── src
       ├── <uc1_input_file1>.cc
       ├── <uc1_input_file2>.cc
       └── <uc1_model_name>
└──  <use_case_name2>
    ├── include
       ├── InputFiles.hpp
       └── Labels.hpp
    └── src
        ├── <uc2_input_file1>.cc
        ├── <uc2_input_file2>.cc
        └── <uc2_model_name>

The next section of the documentation details: Deployment.