Unleash the Power of Microcontroller AI with μTensor: A Tiny, Mighty Machine Learning Framework

Introduction

Over the past few years, machine learning has become a popular and powerful tool for solving complex problems in various fields such as computer vision, speech recognition, and data analysis. With the increasing demand for embedded and Internet of Things (IoT) applications, there is a growing need for applying machine learning techniques on low-power devices such as microcontrollers

μTensor is a powerful tool for performing tensor computations on small devices, such as microcontrollers. Traditional tensor libraries are designed for high-performance machines and are not practical for resource-constrained devices. μTensor addresses this issue by providing a lightweight and efficient solution for tensor computations on small devices.

What is μTensor?

μTensor is an open-source machine learning (ML) framework specifically designed for microcontrollers. It is created and maintained by the TensorFlow team at Google, with the main goal of enabling ML models to run efficiently on resource-constrained devices.

Key features of μTensor include:

• Lightweight and efficient: μTensor is optimized for running on microcontrollers with limited computing power and memory. It has a small memory footprint, minimal dependencies, and efficient code execution, making it ideal for running on devices with low processing power.

• Support for multiple microcontrollers: μTensor supports various microcontrollers, including ARM Cortex-M, Cortex-R, and Cortex-A processors, making it a versatile framework that can be used in a wide range of devices.

• Simple API:μTensor features a simple, easy-to-use API that allows developers to quickly and easily integrate ML functionality into their microcontroller applications. It abstracts away the complexities of ML development, making it more accessible for non-ML experts.

• Cross-platform compatibility: The framework is designed to be cross-platform, allowing developers to write their code once and then deploy it on different types of microcontrollers without any modifications.

• TensorFlow integration: μTensor is built upon TensorFlow libraries, ensuring compatibility with the wider TensorFlow ecosystem. This allows developers to leverage the extensive pre-trained models and tools available in TensorFlow.

μTensor is unique when compared to other ML frameworks for microcontrollers in several ways:

• Size and resource optimization: Most ML frameworks designed for microcontrollers tend to be quite heavy and require a significant amount of resources to run. μTensor, on the other hand, is specifically designed to be small and efficient, making it ideal for resource-constrained devices.

• Versatility: Unlike other frameworks that may be limited to specific microcontroller architectures, μTensor supports a wide range of microcontrollers, making it a versatile choice for developers.

• Simplified API: Other frameworks may require a certain level of expertise in ML concepts and algorithms to use effectively. In contrast, μTensor’s API is designed to be simple and intuitive, making it more accessible for non-experts.

The small footprint and simple API of μTensor offer several advantages for developers working in the field of ML for microcontrollers:

• Reduced development time and costs: μTensor simplifies the process of integrating ML functionality in microcontroller applications, allowing developers to focus on application-specific tasks rather than spending time on complex ML development.

• Improved performance: With its optimized code and efficient execution, μTensor enables ML models to run faster and use fewer resources, resulting in improved performance on resource-constrained devices.

• Increased accessibility: The simplified API of μTensor makes it accessible to developers from a wide range of backgrounds, allowing them to incorporate ML functionality in their microcontroller applications without extensive knowledge of ML.

Getting Started with μTensor

Installation and Setup:

1. μTensor can be installed using pip or manually from source code.

• Using pip: Open a command prompt and type “pip install utensor_cgen” to install μTensor.

• Manually: Download the source code from the μTensor GitHub repository (https://github.com/uTensor/utensor_cgen) and follow the instructions in the README file to build and install it.

2. Once μTensor is installed, you can verify the installation by typing “utensor-cli — version” in the command prompt. This should display the installed version of μTensor.

3. To use μTensor, you will also need to install TensorFlow. μTensor is compatible with TensorFlow versions 1.3 to 1.11.

Basic Usage Examples:

• Converting a TensorFlow model into a μTensor model:

• Create a TensorFlow model (e.g. using a neural network from the TensorFlow tutorial).

• Save the model using the “tf.train.Saver” module.

• Open a command prompt and navigate to the directory containing the saved model. — Type “utensor-cli convert <saved_model_directory> — output-nodes=<output_nodes> — dest-dir=<destination_dir>” to convert the model into a μTensor model.

• The converted model files will be saved in the specified destination directory.

2. Building and running a μTensor model:

• Use the “utensor-cli” command to generate code for your microcontroller platform (e.g. Arduino or Raspberry Pi).

• Include the generated files in your project.

• Add the “utensor-cgen” library to your project’s build settings.

• You can then use the μTensor API to run the model and get output values.

Integrating μTensor into Your Project:

• Create a new project or open an existing one in your preferred integrated development environment (IDE).

• Follow the instructions in the “Basic Usage Examples” section to convert your TensorFlow model into a μTensor model and generate code for your microcontroller platform.

• Include the generated files in your project.

• Add the “utensor-cgen” library to your project’s build settings.

• You can now use the μTensor API to load and run the model, and access the output values.

• Follow the μTensor documentation (https://utensor.readthedocs.io/en/latest/) for further information on how to use the API and customize your model for different microcontroller platforms.