Get started with microcontrollers

This document will help you get started using TensorFlow Lite for Microcontrollers. It explains how to run the framework's example applications, then walks through the code for a simple application that runs inference on a microcontroller.

Get a supported device

To follow this guide, you'll need a supported hardware device. The example application we'll be using has been tested on the following devices:

Learn more about supported platforms in TensorFlow Lite for Microcontrollers.

Explore the examples

TensorFlow Lite for Microcontrollers comes with several example applications that demonstrate its use for various tasks. At the time of writing, the following are available:

  • Hello World - Demonstrates the absolute basics of using TensorFlow Lite for Microcontrollers
  • Micro speech - Captures audio with a microphone in order to detect the words "yes" and "no"
  • Person detection - Captures camera data with an image sensor in order to detect the presence or absence of a person
  • Magic wand - Captures accelerometer data in order to classify three different physical gestures

Each example application has a file that explains how it can be deployed to its supported platforms.

The rest of this guide walks through the Hello World example application.

The Hello World example

This example is designed to demonstrate the absolute basics of using TensorFlow Lite for Microcontrollers. It includes the full end-to-end workflow of training a model, converting it for use with TensorFlow Lite, and running inference on a microcontroller.

In the example, a model is trained to replicate a sine function. It takes a single number as its input, and outputs the number's sine. When deployed to a microcontroller, its predictions are used to either blink LEDs or control an animation.

The example includes the following:

  • A Jupyter notebook that demonstrates how the model is trained and converted
  • A C++ 11 application that runs inference using the model, tested to work with Arduino, SparkFun Edge, STM32F746G discovery kit, and macOS
  • A unit test that demonstrates the process of running inference

Run the example

To run the example on your device, walk through the instructions in the

Hello World

How to run inference

The following section walks through the Hello World example's, unit test which demonstrates how to run inference using TensorFlow Lite for Microcontrollers. It loads the model and runs inference several times.

1. Include the library headers

To use the TensorFlow Lite for Microcontrollers library, we must include the following header files:

#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

2. Include the model header

The TensorFlow Lite for Microcontrollers interpreter expects the model to be provided as a C++ array. The model is defined in model.h and files. The header is included with the following line:

#include "tensorflow/lite/micro/examples/hello_world/model.h"

3. Include the unit test framework header

In order to create a unit test, we include the TensorFlow Lite for Microcontrollers unit test framework by including the following line:

#include "tensorflow/lite/micro/testing/micro_test.h"

The test is defined using the following macros:


TF_LITE_MICRO_TEST(LoadModelAndPerformInference) {
  . // add code here


We now discuss the code included in the macro above.

4. Set up logging

To set up logging, a tflite::ErrorReporter pointer is created using a pointer to a tflite::MicroErrorReporter instance:

tflite::MicroErrorReporter micro_error_reporter;
tflite::ErrorReporter* error_reporter = &micro_error_reporter;

This variable will be passed into the interpreter, which allows it to write logs. Since microcontrollers often have a variety of mechanisms for logging, the implementation of tflite::MicroErrorReporter is designed to be customized for your particular device.

5. Load a model

In the following code, the model is instantiated using data from a char array, g_model, which is declared in model.h. We then check the model to ensure its schema version is compatible with the version we are using:

const tflite::Model* model = ::tflite::GetModel(g_model);
if (model->version() != TFLITE_SCHEMA_VERSION) {
      "Model provided is schema version %d not equal "
      "to supported version %d.\n",
      model->version(), TFLITE_SCHEMA_VERSION);

6. Instantiate operations resolver

An AllOpsResolver instance is declared. This will be used by the interpreter to access the operations that are used by the model:

tflite::AllOpsResolver resolver;

The AllOpsResolver loads all of the operations available in TensorFlow Lite for Microcontrollers, which uses a lot of memory. Since a given model will only use a subset of these operations, it's recommended that real world applications load only the operations that are needed.

This is done using a different class, MicroMutableOpResolver. You can see how to use it in the Micro speech example's

7. Allocate memory

We need to preallocate a certain amount of memory for input, output, and intermediate arrays. This is provided as a uint8_t array of size tensor_arena_size:

const int tensor_arena_size = 2 * 1024;
uint8_t tensor_arena[tensor_arena_size];

The size required will depend on the model you are using, and may need to be determined by experimentation.

8. Instantiate interpreter

We create a tflite::MicroInterpreter instance, passing in the variables created earlier:

tflite::MicroInterpreter interpreter(model, resolver, tensor_arena,
                                     tensor_arena_size, error_reporter);

9. Allocate tensors

We tell the interpreter to allocate memory from the tensor_arena for the model's tensors:


10. Validate input shape

The MicroInterpreter instance can provide us with a pointer to the model's input tensor by calling .input(0), where 0 represents the first (and only) input tensor:

  // Obtain a pointer to the model's input tensor
  TfLiteTensor* input = interpreter.input(0);

We then inspect this tensor to confirm that its shape and type are what we are expecting:

// Make sure the input has the properties we expect
TF_LITE_MICRO_EXPECT_NE(nullptr, input);
// The property "dims" tells us the tensor's shape. It has one element for
// each dimension. Our input is a 2D tensor containing 1 element, so "dims"
// should have size 2.
TF_LITE_MICRO_EXPECT_EQ(2, input->dims->size);
// The value of each element gives the length of the corresponding tensor.
// We should expect two single element tensors (one is contained within the
// other).
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
// The input is a 32 bit floating point value
TF_LITE_MICRO_EXPECT_EQ(kTfLiteFloat32, input->type);

The enum value kTfLiteFloat32 is a reference to one of the TensorFlow Lite data types, and is defined in common.h.

11. Provide an input value

To provide an input to the model, we set the contents of the input tensor, as follows:

input->data.f[0] = 0.;

In this case, we input a floating point value representing 0.

12. Run the model

To run the model, we can call Invoke() on our tflite::MicroInterpreter instance:

TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
  TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed\n");

We can check the return value, a TfLiteStatus, to determine if the run was successful. The possible values of TfLiteStatus, defined in common.h, are kTfLiteOk and kTfLiteError.

The following code asserts that the value is kTfLiteOk, meaning inference was successfully run.

TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);

13. Obtain the output

The model's output tensor can be obtained by calling output(0) on the tflite::MicroInterpreter, where 0 represents the first (and only) output tensor.

In the example, the model's output is a single floating point value contained within a 2D tensor:

TfLiteTensor* output = interpreter.output(0);
TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
TF_LITE_MICRO_EXPECT_EQ(kTfLiteFloat32, output->type);

We can read the value directly from the output tensor and assert that it is what we expect:

// Obtain the output value from the tensor
float value = output->data.f[0];
// Check that the output value is within 0.05 of the expected value
TF_LITE_MICRO_EXPECT_NEAR(0., value, 0.05);

14. Run inference again

The remainder of the code runs inference several more times. In each instance, we assign a value to the input tensor, invoke the interpreter, and read the result from the output tensor:

input->data.f[0] = 1.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(0.841, value, 0.05);

input->data.f[0] = 3.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(0.141, value, 0.05);

input->data.f[0] = 5.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(-0.959, value, 0.05);

15. Read the application code

Once you have walked through this unit test, you should be able to understand the example's application code, located in It follows a similar process, but generates an input value based on how many inferences have been run, and calls a device-specific function that displays the model's output to the user.

Next steps

To understand how the library can be used with a variety of models and applications, we recommend deploying the other examples and walking through their code.

Example applications on GitHub

To learn how to use the library in your own project, read Understand the C++ library.

For information about training and converting models for deployment on microcontrollers, read Build and convert models.