Implementing custom ML application

This section describes how to implement a custom Machine Learning application running on Arm® Corstone™-300 based FVP or on the Arm® MPS3 FPGA prototyping board.

The Arm® Ethos™-U code sample software project offers a way to incorporate more use-case code into the existing infrastructure. It also provides a build system that automatically picks up added functionality and produces corresponding executable for each use-case. This is achieved by following certain configuration and code implementation conventions.

The following sign indicates the important conventions to apply:

Convention: The code is developed using C++14 and C99 standards.

Software project description

See Repository structure section for the outline of the repo.

Convention: Separate use-cases must be organized in sub-folders under the source/use-case folder. The name of the directory is used as a name for this use-case and can be provided as a USE_CASE_BUILD parameter value. The build system expects that sources for the use-case are structured as follows: Headers in an include directory and C/C++ sources in a src directory. For example:

        ├── include
           └── *.hpp
        └── src
            └── *.cc

It is important to note that each use case example has at least one associated API that it uses from source/application/api/use_case. The API sources are platform-agnostic by design so the use cases example implementations can re-use one or more of these components, and they can be used on any target. However, it is not mandatory to use an API, or to implement one if you are adding a use-case.

Hardware Abstraction Layer API

The HAL is represented by the following interfaces. To access them, include the hal.h header.

  • hal_platform_init function: Initializes the HAL platform and every module on the platform that the application requires to run.

    Parameter nameDescription
    returntrue if successful, false otherwise.
  • hal_platform_release function Releases the HAL platform and any acquired resources.

An example of the API initialization in the main function:

#include "hal.h"

int main ()

  /* Initialise the HAL and platform */


  return 0;

Main loop function

Code samples application main function delegates the use-case logic execution to the main loop function that must be implemented for each custom ML scenario.

The main loop function has external linkage and the main executable for the use-case references the function defined in the use-case code.

void main_loop()



Application context

Application context can be used as a holder for a state between main loop iterations. Include AppContext.hpp to use ApplicationContext class.

Method nameDescription
SetSaves given value as a named attribute in the context.
GetGets the saved attribute from the context by the given name.
HasChecks if an attribute with a given name exists in the context.

For example:

#include "hal.h"
#include "AppContext.hpp"

void main_loop()
    /* Instantiate application context */
    arm::app::ApplicationContext caseContext;
    caseContext.Set<uint32_t>("counter", 0);

    /* loop */
    while (true) {
        // do something, pass application context down the call stack


The profiler is a helper class that assists with the collection of timings and Ethos-U cycle counts for operations. It uses platform timer to get system timing information.

Method nameDescription
StartProfilingStarts profiling and records the starting timing data.
StopProfilingStops profiling and records the ending timing data.
StopProfilingAndResetStops the profiling and internally resets the platform timers.
ResetResets the profiler and clears all collected data.
GetAllResultsAndResetGets all the results as string and resets the profiler.
PrintProfilingResultPrints collected profiling results and resets the profiler.
SetNameSet the profiler name.

An example of it in use:

/* A named profiler instance */
Profiler profiler{"Inference"};

// Code running inference to profile


NN Model API

The Model, which refers to neural network model, is an abstract class wrapping the underlying TensorFlow Lite Micro API. It provides methods to perform common operations such as TensorFlow Lite Micro framework initialization, inference execution, accessing input, and output tensor objects.

To use this abstraction, import the Model.hpp header.

Method nameDescription
GetInputTensorReturns the pointer to the model's input tensor.
GetOutputTensorReturns the pointer to the model's output tensor
GetTypeReturns the model's data type
GetInputShapeReturn the pointer to the model's input shape
GetOutputShapeReturn the pointer to the model's output shape.
GetNumInputsReturn the number of input tensors the model has.
GetNumOutputsReturn the number of output tensors the model has.
LogTensorInfoLogs the tensor information to stdout for the given tensor pointer. Includes: Tensor name, tensor address, tensor type, tensor memory size, and quantization params.
LogInterpreterInfoLogs the interpreter information to stdout.
InitInitializes the TensorFlow Lite Micro framework, allocates require memory for the model.
GetAllocatorGets the allocator pointer for the instance.
IsInitedChecks if this model object has been initialized.
IsDataSignedChecks if the model uses signed data type.
RunInferenceRuns the inference, so invokes the interpreter.
ShowModelInfoHandlerModel information handler common to all models.
GetTensorArenaReturns pointer to memory region to be used for tensors allocations.
ModelPointerReturns the pointer to the NN model data array.
ModelSizeReturns the model size.
GetOpResolverReturns the reference to the TensorFlow Lite Micro operator resolver.
EnlistOperationsRegisters required operators with TensorFlow Lite Micro operator resolver.
GetActivationBufferSizeReturns the size of the tensor arena memory region.

Convention: Each ML use-case must have an extension of this class and an implementation of the protected virtual methods:

virtual const tflite::MicroOpResolver& GetOpResolver() = 0;
virtual bool EnlistOperations() = 0;
virtual size_t GetActivationBufferSize() = 0;

Network models have different set of operators that must be registered with tflite::MicroMutableOpResolver object in the EnlistOperations method. Network models can require different size of activation buffer that is returned as tensor arena memory for TensorFlow Lite Micro framework by the GetTensorArena and GetActivationBufferSize methods.

Note: Please see MobileNetModel.hpp and files from the image classification ML application API as an example of the model base class extension.

Adding custom ML use-case

This section describes how to implement additional use-case and then compile it into the binary executable to run with Fast Model or MPS3 FPGA board.

It covers common major steps: The application main loop creation, a description of the NN model, and inference execution.

In addition, few useful examples are provided: Reading user input, printing into console, and drawing images into MPS3 LCD.

For example:

      ├── include
      └── src

Start with creation of a subdirectory under the source/use_case directory and two additional directories src and include as described in the Software project description section.

Implementing main loop

The use-case main loop is the place to put use-case main logic. It is an infinite loop that reacts on user input, triggers use-case conditional logic based on the input and present results back to the user.

However, it could also be a simple logic that runs a single inference and then exits.

Main loop has knowledge about the platform and has access to the platform components through the Hardware Abstraction Layer (HAL).

Start by creating a file in the src directory (the one created under Adding custom ML use-case). The name used is not important.

Now define the main_loop function with the signature described in Main loop function:

#include "hal.h"
#include "log_macros.h"

void main_loop()
    printf("Hello world!");

The preceding code is already a working use-case. If you compile and run it (see Building custom use-case), then the application starts and prints a message to console and exits straight away.

You can now start filling this function with logic.

Implementing custom NN model

Before inference could be run with a custom NN model, TensorFlow Lite Micro framework must learn about the operators, or layers, included in the model. You must register operators using the MicroMutableOpResolver API.

The Ethos-U code samples project has an abstraction around TensorFlow Lite Micro API (see NN model API). Create HelloWorldModel.hpp in the use-case include subdirectory, extend Model abstract class, and then declare the required methods.

For example:


#include "Model.hpp"

namespace arm {
namespace app {

class HelloWorldModel: public Model {
    /** @brief   Gets the reference to op resolver interface class. */
    const tflite::MicroOpResolver& GetOpResolver() override;

    /** @brief   Adds operations to the op resolver instance. */
    bool EnlistOperations() override;

    /* Maximum number of individual operations that can be enlisted. */
    static constexpr int ms_maxOpCnt = 5;

    /* A mutable op resolver instance. */
    tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
} /* namespace app */
} /* namespace arm */


Create the file in the src subdirectory and define the methods there. Include HelloWorldModel.hpp created earlier.

Note: The Model.hpp included in the header provides access to TensorFlow Lite Micro's operation resolver API.

Please refer to use_case/img_class/src/ for code examples.

If you are using a TensorFlow Lite model compiled with Vela, it is important to add a custom Ethos-U operator to the operators list.

The following example shows how to add the custom Ethos-U operator with the TensorFlow Lite Micro framework. when defined, ARM_NPU excludes the code if the application was built without NPU support.

For example:

#include "HelloWorldModel.hpp"
#include "log_macros.h"

bool arm::app::HelloWorldModel::EnlistOperations() {

#if defined(ARM_NPU)
    if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
        info("Added %s support to op resolver\n",
    } else {
        printf_err("Failed to add Arm NPU support to op resolver.");
        return false;
#endif /* ARM_NPU */

    return true;

To minimize the memory footprint of the application, we advise you to only register operators that are used by the NN model.

Using GetModelPointer and GetModelLen methods

These functions generated in the C++ file containing the neural network model as an array. This logic for generation of the C++ array from the .tflite file needs to be defined in the usecase.cmake file for this HelloWorld example. In the root of the source/use_case/hello_world, create a file called usecase.cmake and add the following lines to it:

# Generate model file
            "NN model tflite path"

        MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH}
        NAMESPACE   "arm" "app" "hello_world")

Use the ${use-case}_MODEL_TFLITE_PATH CMake configuration parameter to include custom model in the generation or compilation process. Please refer to: Build options for further information.

For more details on usecase.cmake, refer to: Building options.

For details on code generation flow in general, refer to: Automatic file generation.

The TensorFlow Lite model data is read during the Model::Init method execution. Please refer to source/application/api/common/source/ for more details.

Model::Init will need the pointer to the model. The arm::app::hello_world::GetModelPointer() function is generated during the build and can be found in the file <build>/generated/hello_world/src/<model_file_name>.cc. The file generated is automatically added to the compilation.

At the top of, add:

namespace arm {
namespace app {
    namespace hello_world {

        extern uint8_t* GetModelPointer();
        extern size_t GetModelLen();
    } /* namespace hello_world */

} /* namespace app */
} /* namespace arm */

These functions can now be used in the Model.Init call.

Executing inference

To run an inference successfully, you must use:

  • A TensorFlow Lite model file,
  • An extended Model class,
  • A place to add the code to invoke inference,
  • A main loop function,
  • And some input data.

For the hello_world example below the input array is not populated. However, for real-world deployment this data must either be read from an on-board device or be prepared in the form of C++ sources and baked into the application before compilation.

For example, the image classification application requires extra build steps to generate C++ sources from the provided images with generate_images_code CMake function.

Note: Check that the input data type for your NN model and input array data type are the same. For example, generated C++ sources for images store image data as a uint8 array. For models that were quantized to an int8 data type, convert the image data to int8 correctly before inference execution. Converting asymmetric data to symmetric data involves positioning the zero value. In other words, subtracting an offset for uint8 values. Please check the image classification application source for the code example, such as the ConvertImgToInt8 function.

The following code adds inference invocation to the main loop function:

#include "hal.h"
#include "log_macros.h"
#include "HelloWorldModel.hpp"

namespace arm {
namespace app {
    namespace hello_world {

        extern uint8_t* GetModelPointer();
        extern size_t GetModelLen();
    } /* namespace hello_world */

} /* namespace app */
} /* namespace arm */

void main_loop()
    printf("Hello world!");

    arm::app::HelloWorldModel model;

    /* Load the model. */
    if (!model.Init(arm::app::tensorArena,
                    arm::app::hello_world::GetModelLen())) {
        printf_err("failed to initialise model\n");

    /* Populate input tensors here */
    // Your-custom-code;

    /* Run inference */

    /* Read or post-process output here */
    const uint32_t tensorSz = outputTensor->bytes;
    const uint8_t * outputData = tflite::GetTensorData<uint8>(outputTensor);
    // Your-custom-code;

The code snippet has several important blocks:

  • Creating HelloWorldModel object and initializing it.
  arm::app::HelloWorldModel model;

  /* Load the model */
  if (!model.Init(arm::app::tensorArena,
                  arm::app::hello_world::GetModelLen())) {
      printf_err("failed to initialise model\n");
  • Getting pointers to allocated input and output tensors.

    TfLiteTensor *outputTensor = model.GetOutputTensor();
    TfLiteTensor *inputTensor = model.GetInputTensor();
  • Running inference

  • Reading inference results: Data and data size from the output tensor. We assume that the output layer has a uint8 data type.

    Const uint32_t tensorSz = outputTensor->bytes;
    const uint8_t *outputData = tflite::GetTensorData<uint8>(outputTensor);

To add profiling for the Ethos-U, include a Profiler.hpp header and invoke both StartProfiling and StopProfiling around inference execution.

For example:

Profiler profiler{"Inference"};



Printing to console

The preceding examples used some function to print messages to the console. To use them, include log_macros.h header.

However, for clarity, here is the full list of available functions:

  • printf
  • trace - printf wrapper for tracing messages.
  • debug - printf wrapper for debug messages.
  • info - printf wrapper for informational messages.
  • warn - printf wrapper for warning messages.
  • printf_err - printf wrapper for error messages.

printf wrappers can be switched off with LOG_LEVEL define:

trace (0) < debug (1) < info (2) < warn (3) < error (4).

Note: The default output level is info = level 2.

Reading user input from console

The platform package under HAL must provide an implementation for a function GetLine. This is then wrapped by HAL to expose a function called hal_get_user_input.

char ch_input[128];
hal_get_user_input(ch_input, sizeof(ch_input));

The function intends to block until a line has been provided. For embedded targets, this call might be redirected to get input from a UART block. For the host targets, this will just be a call to the C standard library instead.

Output to MPS3 LCD

The HAL exposes LCD functions to print text or an image to the board LCD. For example:

  • hal_lcd_display_text
  • hal_lcd_display_image

Text presentation function has the following signature:

  • const char* str: the string to print.
  • const uint32_t str_sz: The string size.
  • const uint32_t pos_x: The x coordinate of the first letter in pixels.
  • const uint32_t pos_y: The y coordinate of the first letter in pixels.
  • const uint32_t alow_multiple_lines: Signals whether the text is allowed to span multiple lines on the screen, or must be truncated to the current line.

This function does not wrap text. If the given string cannot fit on the screen, it goes outside the screen boundary.

Here is an example that prints "Hello world" on the LCD screen:

std::string hello("Hello world");
hal_lcd_display_text(hello.c_str(), hello.size(), 10, 35, 0);

The image presentation function has the following signature:

  • uint8_t* data: The image data pointer;
  • const uint32_t width: The image width;
  • const uint32_t height: The image height;
  • const uint32_t channels: The number of channels. Only 1 and 3 channels are supported now.
  • const uint32_t pos_x: The x coordinate of the first pixel.
  • const uint32_t pos_y: The y coordinate of the first pixel.
  • const uint32_t downsample_factor: The factor by which the image is to be downsampled.

For example, the following code snippet visualizes an input tensor data for MobileNet v2 224, by down-sampling it by a factor of two:

hal_lcd_display_image((uint8_t *) inputTensor->, 224, 224, 3, 10, 35, 2);

Please refer to the Hardware Abstraction Layer API section for more LCD related functions.

Building custom use-case

There is one last thing to do before building and running a use-case application. You must create a usecase.cmake file in the root of your use-case. However, the name of the file is not important.

Convention: The build system searches for CMake file in each use-case directory and includes it into the build flow. This file can be used to specify additional application-specific build options, add custom build steps, or override standard compilation and linking flags. Use the USER_OPTION function to add further build options. Prefix the variable name with ${use_case}, the use-case name, to avoid names collisions with other CMake variables. Here are some useful variable names visible in use-case CMake file:

  • DEFAULT_MODEL_PATH – The default model path to use if use-case specific ${use_case}_MODEL_TFLITE_PATH is not set in the build arguments.
  • TARGET_NAME – The name of the executable.
  • use_case – The name of the current use-case.
  • UC_SRC – A list of use-case sources.
  • UC_INCLUDE – The path to the use-case headers.
  • ETHOS_U_NPU_ENABLED – The flag indicating if the current build supports Ethos™-U NPU.
  • TARGET_PLATFORM – The target platform being built for.
  • TARGET_SUBSYSTEM – If target platform supports multiple subsystems, this is the name of the subsystem.
  • All standard build options.
    • CMAKE_CXX_FLAGS and CMAKE_C_FLAGS – The compilation flags.
    • CMAKE_EXE_LINKER_FLAGS – The linker flags.

For the hello world use-case, it is enough to create a helloworld.cmake file and set the DEFAULT_MODEL_PATH, like:

  set(DEFAULT_MODEL_PATH  ${DEFAULT_MODEL_DIR}/helloworldmodel_vela_${DEFAULT_NPU_CONFIG_ID}.tflite)
  set(DEFAULT_MODEL_PATH  ${DEFAULT_MODEL_DIR}/helloworldmodel.tflite)

This can be used in subsequent section, for example:

USER_OPTION(${use_case}_MODEL_TFLITE_PATH "Neural network model in tflite format."


This ensures that the model path pointed to by ${use_case}_MODEL_TFLITE_PATH is converted to a C++ array and is picked up by the build system. More information on auto-generations is available under section: Automatic file generation.

To build you application, follow the general instructions from Add Custom inputs and then specify the name of the use-case in the build command, like so:

cmake .. \
  -DUSE_CASE_BUILD=hello_world \

As a result, the file ethos-u-hello_world.axf is created. The MPS3 build also produces the sectors/hello_world directory with binaries and the file sectors/images.txt to be copied to the MicroSD card on the board.

The next section of the documentation covers: Testing and benchmarking.

Adding custom platform support

Platform build configuration script build_configuration.cmake is the main build entry point for platform sources. It is used by top level CMakeLists.txt script to add a platform into the public build stream. Platform build configuration script must have 2 functions:

  • set_platform_global_defaults - to set platform source locations and other build options.
  • platform_custom_post_build - to execute specific post build steps.

The function set_platform_global_defaults must set PLATFORM_DRIVERS_DIR variable


location of the platform library sources.

Convention: The default search path for platform build configuration scripts is in scripts/cmake/platforms. The directory name for a platform configuration script is also used as a value for TARGET_PLATFORM build option. For example: scripts/cmake/platforms/my_platform results in having my_platform as a TARGET_PLATFORM option for the build.

The function platform_custom_post_build could be used to add platform specific post use-case application build steps.

Repository's root level CMakeLists.txt calls common utility function

add_platform_build_configuration(TARGET_PLATFORM ${TARGET_PLATFORM})

to add given target platform to the build stream. The function finds the script and includes build_configuration.cmake file. After that public build can invoke:

  • set_platform_global_defaults
  • platform_custom_post_build

for a specified platform.

New platform sources, that are pointed to by PLATFORM_DRIVERS_DIR variable, could be placed anywhere, conventional location is source/hal/platform. Platform must be a separate CMake project with CMakeLists.txt script and build into a static library libplatform-drivers.a. HAL expects platform to have platfrom_drivers.h header file with required interfaces for included peripherals.

If the new platform uses existing cmsis device project then it should be linked with it like this:

    target_link_libraries(${PLATFORM_DRIVERS_TARGET} PUBLIC cmsis_device)

Cmsis device exposes an entry point --entry Reset_Handler as a link interface.

If the new platform defines custom cmsis device and has custom application entry point, it must tell linker about it like this:

    target_link_options(${PLATFORM_DRIVERS_TARGET} INTERFACE --entry <custom handler name>)

Most of the ML use-case applications use UART driven standard output (stdout), and LCD. It is therefore a hard requirement to implement at least stubs for those. LCD driver must provide implementation for functions declared in lcd_img.h header. The LCD component under HAL sources provides an implementation for MPS3's LCD peripheral as well as a stub. The application linking to the library produced by LCD has a choice of picking either target. For example, the MPS3 platform implementation has:

target_link_libraries(${PLATFORM_DRIVERS_TARGET} PUBLIC
    <other libs>

The implementation for simple platform on the other hand has:

target_link_libraries(${PLATFORM_DRIVERS_TARGET} PUBLIC
    <other libs>

The standard output (stdout) component follows the same convention. It can expose three targets:

  • stdout_retarget_cmsdk
  • stdout_retarget_pl011
  • stdout

The first two targets use the UART (pulling in CMSDK UART and PL011 UART drivers respectively). The third implementation relies on standard C or overridden implementation of fgets function and can be thought of as stubs for platforms that do not need to use UART driven stdout (and stderr) streams. It is also possible to run applications with semi-hosting enabled - printf statements will be shown in the host machine console, typically via a debugger. To facilitate this, the CMake toolchain files expose a function called configure_semihosting. For supported targets semi-hosting is disabled by default, as mentioned in the cmsis_device target CMake file.

configure_semihosting(${CMSIS_DEVICE_TARGET} OFF)

Other re-usable component is the NPU. It wraps the Arm Ethos-U NPU driver sources with functions that can be called from the platform initialisation routine. In addition to general utility functions, this component also provides cache invalidation overrides for the weak implementations available in the driver. This is useful for custom targets that use the data cache for the target CPU (typically enabled somewhere in the start-up code for the system). The driver will call these functions for invalidating data cache (memory regions the NPU has written to).

Note: To do the Arm Ethos-U interrupt set up, the NPU component should be provided with the base address of the NPU as well as the IRQ number wired to the CPU. To set this up, the NPU component relies on the system specific header files made available by the CMake interface target provided by the cmsis_device CMake project. If this target is not used by the new custom platform implementation, the NPU component target must be given access to an "RTE_Components.h" header in some way. This header should pull in the required definitions for functions like SCB_InvalidateDCache and NVIC_SetVector. The pre-processor definitions like __DCACHE_PRESENT are also expected to be exposed via the same header.

Examples of the standard output, LCD and NPU components can be found here: source/hal/source/components.

Linker scripts for Arm Compiler and GNU embedded toolchain should be added. The location of the files is on your discretion. The new platform build configuration script must add it in the platform_custom_post_build function like this:

            ${PARSED_TARGET_NAME}                  # Target
            ${CMAKE_SCRIPTS_DIR}/platforms/mps3    # linker scripts directory path
            ${LINKER_SCRIPT_NAME})                 # Name of the file without suffix

Please see existing platforms sources and build scripts for more details.