The LiteRT for Microcontrollers C++ library is part of the
[TensorFlow repository](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro).
It is designed to be readable, easy to modify, well-tested, easy to integrate,
and compatible with regular LiteRT.

The following document outlines the basic structure of the C++ library and
provides information about creating your own project.

## File structure

The
[`micro`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro)
root directory has a relatively simple structure. However, since it is located
inside of the extensive TensorFlow repository, we have created scripts and
pre-generated project files that provide the relevant source files in isolation
within various embedded development environments.

### Key files

The most important files for using the LiteRT for Microcontrollers
interpreter are located in the root of the project, accompanied by tests:

    [`micro_mutable_op_resolver.h`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/micro_mutable_op_resolver.h)
    can be used to provide the operations used by the interpreter to run the
    model.

- [`micro_error_reporter.h`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h) outputs debug information.
- [`micro_interpreter.h`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/micro_interpreter.h) contains code to handle and run models.

See [Get started with microcontrollers](https://developers.google.com/edge/litert/microcontrollers/get_started) for a
walkthrough of typical usage.

The build system provides for platform-specific implementations of certain
files. These are located in a directory with the platform name, for example
[`cortex-m`](https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/cortex_m_generic).

Several other directories exist, including:

- [`kernel`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/kernels), which contains operation implementations and the associated code.
- [`tools`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/tools), which contains build tools and their output.
- [`examples`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples), which contains sample code.

## Start a new project

We recommend using the *Hello World* example as a template for new projects. You
can obtain a version of it for your platform of choice by following the
instructions in this section.

### Use the Arduino library

If you are using Arduino, the *Hello World* example is included in the
`Arduino_TensorFlowLite` Arduino library, which you can manually install in the
Arduino IDE and in [Arduino Create](https://create.arduino.cc/).

Once the library has been added, go to `File -> Examples`. You should see an
example near the bottom of the list named `TensorFlowLite:hello_world`. Select
it and click `hello_world` to load the example. You can then save a copy of the
example and use it as the basis of your own project.

### Generate projects for other platforms

LiteRT for Microcontrollers is able to generate standalone projects
that contain all of the necessary source files, using a `Makefile`. The current
supported environments are Keil, Make, and Mbed.

To generate these projects with Make, clone the
[TensorFlow/tflite-micro repository](https://github.com/tensorflow/tflite-micro)
and run the following command:

    make -f tensorflow/lite/micro/tools/make/Makefile generate_projects

This will take a few minutes, since it has to download some large toolchains for
the dependencies. Once it has finished, you should see some folders created
inside a path like `gen/linux_x86_64/prj/` (the
exact path depends on your host operating system). These folders contain the
generated project and source files.

After running the command, you'll be able to find the *Hello World* projects in
`gen/linux_x86_64/prj/hello_world`. For
example, `hello_world/keil` will contain the Keil project.

## Run the tests

To build the library and run all of its unit tests, use the following command:

    make -f tensorflow/lite/micro/tools/make/Makefile test

To run an individual test, use the following command, replacing `<test_name>`
with the name of the test:

    make -f tensorflow/lite/micro/tools/make/Makefile test_<test_name>

You can find the test names in the project's Makefiles. For example,
`examples/hello_world/Makefile.inc` specifies the test names for the *Hello
World* example.

## Build binaries

To build a runnable binary for a given project (such as an example application),
use the following command, replacing `<project_name>` with the project you wish
to build:

    make -f tensorflow/lite/micro/tools/make/Makefile <project_name>_bin

For example, the following command will build a binary for the *Hello World*
application:

    make -f tensorflow/lite/micro/tools/make/Makefile hello_world_bin

By default, the project will be compiled for the host operating system. To
specify a different target architecture, use `TARGET=` and `TARGET_ARCH=`. The
following example shows how to build the *Hello World* example for a generic
cortex-m0:

    make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_generic TARGET_ARCH=cortex-m0 hello_world_bin

When a target is specified, any available target-specific source files will be
used in place of the original code. For example, the subdirectory
`examples/hello_world/cortex_m_generic` contains SparkFun Edge implementations
of the files `constants.cc` and `output_handler.cc`, which will be used when the
target `cortex_m_generic` is specified.

You can find the project names in the project's Makefiles. For example,
`examples/hello_world/Makefile.inc` specifies the binary names for the *Hello
World* example.

## Optimized kernels

The reference kernels in the root of `tensorflow/lite/micro/kernels` are
implemented in pure C/C++, and do not include platform-specific hardware
optimizations.

Optimized versions of kernels are provided in subdirectories. For example,
`kernels/cmsis-nn` contains several optimized kernels that make use of Arm's
CMSIS-NN library.

To generate projects using optimized kernels, use the following command,
replacing `<subdirectory_name>` with the name of the subdirectory containing the
optimizations:

    make -f tensorflow/lite/micro/tools/make/Makefile TAGS=<subdirectory_name> generate_projects

You can add your own optimizations by creating a new subfolder for them. We
encourage pull requests for new optimized implementations.

## Generate the Arduino library

If you need to generate a new build of the library, you can run the following
script from the TensorFlow repository:

    ./tensorflow/lite/micro/tools/ci_build/test_arduino.sh

The resulting library can be found in
`gen/arduino_x86_64/prj/tensorflow_lite.zip`.

## Port to new devices

Guidance on porting LiteRT for Microcontrollers to new platforms and
devices can be found in
[`micro/docs/new_platform_support.md`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/docs/new_platform_support.md).