path: root/docs/sections/customizing.md

# Implementing custom ML application

## Contents

- [Software project description](#software-project-description)
- [HAL API](#hal-api)
- [Main loop function](#main-loop-function)
- [Application context](#application-context)
- [Profiler](#profiler)
- [NN Model API](#nn-model-api)
- [Adding custom ML use case](#adding-custom-ml-use-case)
- [Implementing main loop](#implementing-main-loop)
- [Implementing custom NN model](#implementing-custom-nn-model)
- [Executing inference](#executing-inference)
- [Printing to console](#printing-to-console)
- [Reading user input from console](#reading-user-input-from-console)
- [Output to MPS3 LCD](#output-to-mps3-lcd)
- [Building custom use case](#building-custom-use-case)

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.

Arm® Ethos™-U55 code sample software project offers a simple way to incorporate
additional use-case code into the existing infrastructure and 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 will indicate the important conventions to apply:

> **Convention:** The code is developed using C++11 and C99 standards.
> This is governed by TensorFlow Lite for Microcontrollers framework.

## Software project description

As mentioned in the [Repository structure](../documentation.md#repository-structure) section, project sources are:

```tree
├── docs
│ ├── ...
│ └── Documentation.md
├── resources
│ └── img_class
│      └── ...
├── scripts
│ └── ...
├── source
│ ├── application
│ │ ├── hal
│ │ ├── main
│ │ └── tensorflow-lite-micro
│ └── use_case
│     └──img_class
├── CMakeLists.txt
└── Readme.md
```

Where `source` contains C/C++ sources for the platform and ML applications.
Common code related to the Ethos-U55 code samples software
framework resides in the `application` sub-folder and ML application specific logic (use-cases)
sources are in the `use-case` subfolder.

> **Convention**: Separate use-cases must be organized in sub-folders under the use-case folder.
> The name of the directory is used as a name for this use-case and could be provided
> as a `USE_CASE_BUILD` parameter value.
> It is expected by the build system that sources for the use-case are structured as follows:
> headers in an `include` directory, C/C++ sources in a `src` directory.
> For example:
>
>```tree
>use_case
> └──img_class
>       ├── include
>       │   └── *.hpp
>       └── src
>           └── *.cc
>```

## HAL API

Hardware abstraction layer is represented by the following interfaces.
To access them, include `hal.h` header.

- `hal_platform` structure:
    Structure that defines a platform context to be used by the application

  |  Attribute name    | Description |
  |--------------------|----------------------------------------------------------------------------------------------|
  |  inited            |  Initialization flag. Is set after the platform_init() function is called.                   |
  |  plat_name         |  Platform name. it is set to "mps3-bare" for MPS3 build and "FVP" for Fast Model build.      |
  |  data_acq          |  Pointer to data acquisition module responsible for user interaction and other data collection for the application logic.               |
  |  data_psn          |  Pointer to data presentation module responsible for data output through components available in the selected platform: LCD -- for MPS3, console -- for Fast Model. |
  |  timer             |  Pointer to platform timer implementation (see platform_timer)                               |
  |  platform_init     |  Pointer to platform initialization function.                                                |
  |  platform_release  |  Pointer to platform release function                                                        |

- `hal_init` function:
    Initializes the HAL structure based on compile time config. This
    should be called before any other function in this API.

  |  Parameter name  | Description|
  |------------------|-----------------------------------------------------|
  |  platform        | Pointer to a pre-allocated `hal_platform` struct.   |
  |  data_acq        | Pointer to a pre-allocated data acquisition module  |
  |  data_psn        | Pointer to a pre-allocated data presentation module |
  |  timer           | Pointer to a pre-allocated timer module             |
  |  return          | zero if successful, error code otherwise            |

- `hal_platform_init` function:
  Initializes the HAL platform and all the modules on the platform the
  application requires to run.

  | Parameter name  | Description                                                         |
  | ----------------| ------------------------------------------------------------------- |
  | platform        | Pointer to a pre-allocated and initialized `hal_platform` struct.   |
  | return          | zero if successful, error code otherwise.                           |

- `hal_platform_release` function
  Releases the HAL platform. This should release resources acquired.

  | Parameter name  | Description                                                         |
  | ----------------| ------------------------------------------------------------------- |
  |  platform       | Pointer to a pre-allocated and initialized `hal_platform` struct.   |

- `data_acq_module` structure:
  Structure to encompass the data acquisition module and it's methods.

  | Attribute name | Description                                        |
  |----------------|----------------------------------------------------|
  | inited         | Initialization flag. Is set after the system_init () function is called. |
  | system_name    | Channel name. It is set to "UART" for MPS3 build and fastmodel builds.   |
  | system_init    | Pointer to data acquisition module initialization function. The pointer is set according to the platform selected during the build. This function is called by the platforminitialization routines. |
  | get_input      | Pointer to a function reading user input. The pointer is set according to the selected platform during the build. For MPS3 and fastmodel environments, the function reads data from UART.   |

- `data_psn_module` structure:
  Structure to encompass the data presentation module and its methods.

  | Attribute name     | Description                                    |
  |--------------------|------------------------------------------------|
  | inited             | Initialization flag. It is set after the system_init () function is called. |
  | system_name        | System component name used to present data. It is set to "lcd" for MPS3 build and to "log_psn" for fastmodel build. In case of fastmodel, all pixel drawing functions are replaced by console output of the data summary.                              |
  | system_init        | Pointer to data presentation module initialization function. The pointer is set according to the platform selected during the build. This function is called by the platform initialization routines. |
  | present_data_image | Pointer to a function to draw an image. The pointer is set according to the selected platform during the build. For MPS3, the image will be drawn on the LCD; for fastmodel  image summary will be printed in the UART  (coordinates, channel info, downsample factor) |
  | present_data_text  | Pointer to a function to print a text. The pointer is set according to the selected platform during the build. For MPS3, the text will be drawn on the LCD; for fastmodel text will be printed in the UART. |
  | present_box        | Pointer to a function to draw a rectangle. The pointer is set according to the selected platform during the build. For MPS3, the image will be drawn on the LCD; for fastmodel  image summary will be printed in the UART. |
  | clear              | Pointer to a function to clear the output. The pointer is set according to the selected platform during the build. For MPS3, the function will clear the LCD; for fastmodel will do nothing. |
  | set_text_color     | Pointer to a function to set text color for the next call of present_data_text() function. The pointer is set according to the selected platform during the build. For MPS3, the function will set the color for the text printed on the LCD; for fastmodel -- will do nothing. |

- `platform_timer` structure:
    Structure to hold a platform specific timer implementation.

  | Attribute name      | Description                                    |
  |---------------------|------------------------------------------------|
  |  inited             |  Initialization flag. It is set after the timer is initialized by the `hal_platform_init` function. |
  |  reset              |  Pointer to a function to reset a timer. |
  |  get_time_counter   |  Pointer to a function to get current time counter. |
  |  get_duration_ms    |  Pointer to a function to calculate duration between two time-counters in milliseconds. |
  |  get_duration_us    |  Pointer to a function to calculate duration between two time-counters in microseconds |
  |  get_cpu_cycle_diff |  Pointer to a function to calculate duration between two time-counters in Cortex-M55 cycles. |
  |  get_npu_cycle_diff |  Pointer to a function to calculate duration between two time-counters in Ethos-U55 cycles. Available only when project is configured with ETHOS_U55_ENABLED set. |
  |  start_profiling    |  Wraps `get_time_counter` function with additional profiling initialisation, if required. |
  |  stop_profiling     |  Wraps `get_time_counter` function along with additional instructions when profiling ends, if required. |

Example of the API initialization in the main function:

```c++
#include "hal.h"

int main ()

{

  hal_platform platform;
  data_acq_module dataAcq;
  data_psn_module dataPsn;
  platform_timer timer;

  /* Initialise the HAL and platform */
  hal_init(&platform, &dataAcq, &dataPsn, &timer);
  hal_platform_init(&platform);

  ...

  hal_platform_release(&platform);

  return 0;

}
```

## Main loop function

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

Main loop function takes the initialized *hal_platform* structure
pointer as an argument.

The main loop function has external linkage and main executable for the
use-case will have reference to the function defined in the use-case
code.

```c++
void main_loop(hal_platform& platform){

...

}
```

## Application context

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

| Method name  | Description                                                      |
|--------------|------------------------------------------------------------------|
|  Set         |  Saves given value as a named attribute in the context.          |
|  Get         |  Gets the saved attribute from the context by the given name.    |
|  Has         |  Checks if an attribute with a given name exists in the context. |

For example:

```c++
#include "hal.h"
#include "AppContext.hpp"

void main_loop(hal_platform& platform) {

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

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

## Profiler

Profiler is a helper class assisting in collection of timings and
Ethos-U55 cycle counts for operations. It uses platform timer to get
system timing information.

| Method name             | Description                                                    |
|-------------------------|----------------------------------------------------------------|
|  StartProfiling         | Starts profiling and records the starting timing data.         |
|  StopProfiling          | Stops profiling and records the ending timing data.            |
|  StopProfilingAndReset  | Stops the profiling and internally resets the platform timers. |
|  Reset                  | Resets the profiler and clears all collected data.             |
|  GetAllResultsAndReset  | Gets all the results as string and resets the profiler.            |
|  PrintProfilingResult   | Prints collected profiling results and resets the profiler.    |
|  SetName                | Set the profiler name.                                         |

Usage example:

```c++
Profiler profiler{&platform, "Inference"};

profiler.StartProfiling();
// Code running inference to profile
profiler.StopProfiling();

profiler.PrintProfilingResult();
```

## NN Model API

Model (refers to neural network model) is an abstract class wrapping the
underlying TensorFlow Lite Micro API and providing 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 TensorFlowLiteMicro.hpp header.

| Method name              | Description                                                                  |
|--------------------------|------------------------------------------------------------------------------|
|  GetInputTensor          |  Returns the pointer to the model's input tensor.                            |
|  GetOutputTensor         |  Returns the pointer to the model's output tensor                            |
|  GetType                 |  Returns the model's data type                                               |
|  GetInputShape           |  Return the pointer to the model's input shape                               |
|  GetOutputShape          |  Return the pointer to the model's output shape.                             |
|  GetNumInputs            |  Return the number of input tensors the model has.                           |
|  GetNumOutputs           |  Return the number of output tensors the model has.                          |
|  LogTensorInfo           |  Logs the tensor information to stdout for the given tensor pointer: tensor name, tensor address, tensor type, tensor memory size and quantization params.  |
|  LogInterpreterInfo      |  Logs the interpreter information to stdout.                                 |
|  Init                    |  Initializes the TensorFlow Lite Micro framework, allocates require memory for the model. |
|  GetAllocator            |  Gets the allocator pointer for the instance.                                |
|  IsInited                |  Checks if this model object has been initialized.                           |
|  IsDataSigned            |  Checks if the model uses signed data type.                                  |
|  RunInference            |  Runs the inference (invokes the interpreter).                               |
|  ShowModelInfoHandler    |  Model information handler common to all models.                             |
|  GetTensorArena          |  Returns pointer to memory region to be used for tensors allocations.        |
|  ModelPointer            |  Returns the pointer to the NN model data array.                             |
|  ModelSize               |  Returns the model size.                                                     |
|  GetOpResolver           |  Returns the reference to the TensorFlow Lite Micro operator resolver.       |
|  EnlistOperations        |  Registers required operators with TensorFlow Lite Micro operator resolver.  |
|  GetActivationBufferSize |  Returns the size of the tensor arena memory region.                         |

> **Convention:**  Each ML use-case must have extension of this class and implementation of the protected virtual methods:
>
>```c++
> virtual const uint8_t* ModelPointer() = 0;
> virtual size_t ModelSize() = 0;
> 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 could require different size of activation buffer that is returned as
> tensor arena memory for TensorFlow Lite Micro framework by the GetTensorArena
> and GetActivationBufferSize methods.

Please see `MobileNetModel.hpp` and `MobileNetModel.cc` files from image
classification ML application use-case as an example of the model base
class extension.

## Adding custom ML use case

This section describes how to implement additional use-case and compile
it into the binary executable to run with Fast Model or MPS3 FPGA board.
It covers common major steps: application main loop creation,
description of the NN model, inference execution.

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

```tree
use_case
   └──hello_world
      ├── include
      └── src
```

Start with creation of a sub-directory under the `use_case` directory and
two other directories `src` and `include` as described in
[Software project description](#software-project-description) section:

## Implementing main loop

Use-case main loop is the place to put use-case main logic. Essentially,
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 (referred to as HAL).

Create a `MainLoop.cc` file in the `src` directory (the one created under
[Adding custom ML use case](#adding-custom-ml-use-case)), the name is not
important. Define `main_loop` function with the signature described in
[Main loop function](#main-loop-function):

```c++
#include "hal.h"

void main_loop(hal_platform& platform) {
  printf("Hello world!");
}
```

The above is already a working use-case, if you compile and run it (see
[Building custom usecase](#Building-custom-use-case)) the application will start, print
message to console and exit straight away.

Now, you can 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/layers included in the
model. Developer must register operators using `MicroMutableOpResolver`
API.

Ethos-U55 code samples project has an abstraction around TensorFlow
Lite Micro API (see [NN model API](#nn-model-api)). Create `HelloWorldModel.hpp` in
the use-case include sub-directory, extend Model abstract class and
declare required methods.

For example:

```c++
#ifndef HELLOWORLDMODEL_HPP
#define HELLOWORLDMODEL_HPP

#include "Model.hpp"

namespace arm {
namespace app {

class HelloWorldModel: public Model {
  protected:
    /** @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;

    const uint8_t* ModelPointer() override;

    size_t ModelSize() override;

  private:
    /* 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 */

#endif /* HELLOWORLDMODEL_HPP */
```

Create `HelloWorldModel.cc` file in the `src` sub-directory and define the methods
there. Include `HelloWorldModel.hpp` created earlier. Note that `Model.hpp`
included in the header provides access to TensorFlow Lite Micro's operation
resolver API.

Please, see `use_case/img_class/src/MobileNetModel.cc` for
code examples.
If you are using a TensorFlow Lite model compiled with Vela, it is important to add
custom Ethos-U55 operator to the operators list.

The following example shows how to add the custom Ethos-U55 operator with
TensorFlow Lite Micro framework. We will use the ARM_NPU define to exclude
the code if the application was built without NPU support.

```c++
#include "HelloWorldModel.hpp"

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

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

    return true;
}
```

To minimize application memory footprint, it is advised to register only
operators used by the NN model.

Define `ModelPointer` and `ModelSize` methods. These functions are wrappers around the
functions generated in the C++ file containing the neural network model as an array.
This generation the C++ array from the .tflite file, logic needs to be defined in
the `usecase.cmake` file for this `HelloWorld` example.

For more details on `usecase.cmake`, see [Building custom use case](#building-custom-use-case).
For details on code generation flow in general, see [Automatic file generation](./building.md#Automatic-file-generation)

The TensorFlow Lite model data is read during Model::Init() method execution, see
`application/tensorflow-lite-micro/Model.cc` for more details. Model invokes
`ModelPointer()` function which calls the `GetModelPointer()` function to get
neural network model data memory address. The `GetModelPointer()` function
will be generated during the build and could be found in the
file `build/generated/hello_world/src/<model_file_name>.cc`. Generated
file is added to the compilation automatically.

Use `${use-case}_MODEL_TFLITE_PATH` build parameter to include custom
model to the generation/compilation process (see [Build options](./building.md/#build-options)).

## Executing inference

To run an inference successfully it is required to have:

- a TensorFlow Lite model file
- extended Model class
- place to add the code to invoke inference
- main loop function
- and some input data.

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

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

> **Note:** Check 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 uint8 array. For models that were
> quantized to int8 data type, it is important to convert image data to int8 correctly before inference execution.
> Asymmetric data type to symmetric data type conversion involves positioning zero value, i.e. subtracting an
> offset for uint8 values. Please check image classification application source for the code example
> (ConvertImgToInt8 function).

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

```c++
#include "hal.h"
#include "HelloWorldModel.hpp"

  void main_loop(hal_platform& platform) {

  /* model wrapper object */
  arm::app::HelloWorldModel model;

  /* Load the model */
  if (!model.Init()) {
    printf_err("failed to initialise model\n");
    return;
  }

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

  /* dummy input data*/
  uint8_t inputData[1000];

  memcpy(inputTensor->data.data, inputData, 1000);

  /* run inference */
  model.RunInference();

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

The code snippet has several important blocks:

- Creating HelloWorldModel object and initializing it.

  ```c++
  arm::app::HelloWorldModel model;

  /* Load the model */
  if (!model.Init()) {
    printf_err(\"failed to initialise model\\n\");
    return;
  }
  ```

- Getting pointers to allocated input and output tensors.

  ```c++
  TfLiteTensor *outputTensor = model.GetOutputTensor();
  TfLiteTensor *inputTensor = model.GetInputTensor();
  ```

- Copying input data to the input tensor. We assume input tensor size
  to be 1000 uint8 elements.

  ```c++
  memcpy(inputTensor->data.data, inputData, 1000);
  ```

- Running inference

  ```c++
  model.RunInference();
  ```

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

  ```c++
  Const uint32_t tensorSz = outputTensor->bytes ;

  const uint8_t *outputData = tflite::GetTensorData<uint8>(outputTensor);
  ```

Adding profiling for Ethos-U55 is easy. Include `Profiler.hpp` header and
invoke `StartProfiling` and `StopProfiling` around inference
execution.

```c++
Profiler profiler{&platform, "Inference"};

profiler.StartProfiling();
model.RunInference();
profiler.StopProfiling();

profiler.PrintProfilingResult();
```

## Printing to console

Provided examples already used some function to print messages to the
console. 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 could be switched off with `LOG_LEVEL` define:

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

Default output level is info = level 2.

## Reading user input from console

Platform data acquisition module has get_input function to read keyboard
input from the UART. It can be used as follows:

```c++
char ch_input[128];
platform.data_acq->get_input(ch_input, sizeof(ch_input));
```

The function will block until user provides an input.

## Output to MPS3 LCD

Platform presentation module has functions to print text or an image to
the board LCD:

- `present_data_text`
- `present_data_image`

Text presentation function has the following signature:

- `const char* str`: string to print.
- `const uint32_t str_sz`: string size.
- `const uint32_t pos_x`: x coordinate of the first letter in pixels.
- `const uint32_t pos_y`: 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 should be truncated
    to the current line.

This function does not wrap text, if the given string cannot fit on the
screen it will go outside the screen boundary.

Example that prints "Hello world" on the LCD:

```c++
std::string hello("Hello world");
platform.data_psn->present_data_text(hello.c_str(), hello.size(), 10, 35, 0);
```

Image presentation function has the following signature:

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

For example, the following code snippet visualizes an input tensor data
for MobileNet v2 224 (down sampling it twice):

```c++
platform.data_psn->present_data_image((uint8_t *) inputTensor->data.data, 224, 224, 3, 10, 35, 2);
```

Please see [hal-api](#hal-api) section for other data presentation
functions.

## Building custom use case

There is one last thing to do before building and running a use-case
application: create a `usecase.cmake` file in the root of your use-case,
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 could be used to specify additional application specific build options, add custom build steps or
> override standard compilation and linking flags.
> Use `USER_OPTION` function to add additional build option. Prefix variable name with `${use_case}` (use-case name) to
> avoid names collisions with other CMake variables.
> Some useful variable names visible in use-case CMake file:
>
> - `DEFAULT_MODEL_PATH` – default model path to use if use-case specific `${use_case}_MODEL_TFLITE_PATH` is not set
>in the build arguments.
>- `TARGET_NAME` – name of the executable.
> - `use_case` – name of the current use-case.
> - `UC_SRC` – list of use-case sources.
> - `UC_INCLUDE` – path to the use-case headers.
> - `ETHOS_U55_ENABLED` – flag indicating if the current build supports Ethos-U55.
> - `TARGET_PLATFORM` – 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` – compilation flags.
>   - `CMAKE_EXE_LINKER_FLAGS` – linker flags.

For the hello world use-case it will be enough to create
`helloworld.cmake` file and set DEFAULT_MODEL_PATH:

```cmake
if (ETHOS_U55_ENABLED EQUAL 1)
  set(DEFAULT_MODEL_PATH  ${DEFAULT_MODEL_DIR}/helloworldmodel_uint8_vela.tflite)
else()
  set(DEFAULT_MODEL_PATH  ${DEFAULT_MODEL_DIR}/helloworldmodel_uint8.tflite)
endif()
```

This can be used in subsequent section, for example:

```cmake
USER_OPTION(${use_case}_MODEL_TFLITE_PATH "Neural network model in tflite format."
    ${DEFAULT_MODEL_PATH}
    FILEPATH
    )

# Generate model file
generate_tflite_code(
    MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH}
    DESTINATION ${SRC_GEN_DIR}
    )
```

This ensures that the model path pointed 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](./building.md#Automatic-file-generation).

To build you application follow the general instructions from
[Add Custom inputs](#add-custom-inputs) and specify the name of the use-case in the
build command:

```commandline
cmake \
  -DTARGET_PLATFORM=mps3 \
  -DTARGET_SUBSYSTEM=sse-300 \
  -DUSE_CASE_BUILD=hello_world \
  -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/bare-metal-toolchain.cmake ..
```

As a result, `ethos-u-hello_world.axf` should be created, MPS3 build
will also produce `sectors/hello_world` directory with binaries and
`images-hello_world.txt` to be copied to the board MicroSD card.

Next section of the documentation: [Testing and benchmarking](../documentation.md#Testing-and-benchmarking).