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Understand the C++ library

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The TensorFlow Lite for Microcontrollers C++ library is part of the TensorFlow repository. It is designed to be readable, easy to modify, well-tested, easy to integrate, and compatible with regular TensorFlow Lite.

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

File structure

The 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 TensorFlow Lite for Microcontrollers interpreter are located in the root of the project, accompanied by tests:

  • all_ops_resolver.h or micro_mutable_op_resolver.h can be used to provide the operations used by the interpreter to run the model. Since all_ops_resolver.h pulls in every available operation, it uses a lot of memory. In production applications, you should use micro_mutable_op_resolver.h to pull in only the operations your model needs.
  • micro_error_reporter.h outputs debug information.
  • micro_interpreter.h contains code to handle and run models.

See Get started with microcontrollers 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 sparkfun_edge.

Several other directories exist, including:

  • kernel, which contains operation implementations and the associated code.
  • tools, which contains build tools and their output.
  • 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.

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

TensorFlow Lite 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 repository 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/ 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=. The following example shows how to build the Hello World example for the SparkFun Edge:

make -f tensorflow/lite/micro/tools/make/Makefile TARGET=sparkfun_edge 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/sparkfun_edge contains SparkFun Edge implementations of the files and, which will be used when the target sparkfun_edge is specified.

You can find the project names in the project's Makefiles. For example, examples/hello_world/ 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:


The resulting library can be found in gen/arduino_x86_64/prj/

Port to new devices

Guidance on porting TensorFlow Lite for Microcontrollers to new platforms and devices can be found in micro/docs/