TinyML (Tiny Machine Learning) is a rapidly growing field that involves running machine learning models on low-power microcontrollers. These microcontrollers are designed to perform AI tasks at the edge while consuming minimal power, making them ideal for a wide range of applications, from smart home devices to wearables and industrial IoT.

In this post, we’ll take a look at some of the most popular TinyML boards available today, including their features, capabilities, and links to learn more.

The OpenMV H7 is a powerful TinyML board designed for computer vision applications. It features an STM32H7 microcontroller, which has a dual-core ARM Cortex-M7 processor running at up to 480 MHz. The board also includes a camera module, various sensors, and support for TensorFlow Lite for Microcontrollers.

With the OpenMV H7, you can run machine learning models for object detection, face recognition, and more. The board is also compatible with the OpenMV IDE, a Python-based development environment that makes it easy to program and deploy your applications.

Learn more about the OpenMV H7: https://openmv.io/products/h7

The Seeed Studio Wio Terminal is a versatile TinyML board that features a 32-bit ARM Cortex-M4F microcontroller, a 2.4-inch LCD screen, and various sensors. It supports TensorFlow Lite for Microcontrollers and can be used for TinyML applications, as well as other projects that require a compact and easy-to-use development board.

The Wio Terminal also includes a built-in microphone and speaker, making it suitable for voice recognition and other audio-related applications. It’s compatible with the Arduino IDE and supports Grove modules, allowing you to easily expand its capabilities.

Learn more about the Seeed Studio Wio Terminal: https://www.seeedstudio.com/Wio-Terminal-p-4509.html

The STM32 Nucleo is a family of development boards based on the STM32 microcontroller series. These boards come in various configurations and are suitable for a wide range of applications, including TinyML.

Some STM32 microcontrollers, like the STM32H7 series, have built-in machine learning acceleration, making them ideal for running TensorFlow Lite for Microcontrollers. The STM32 Nucleo boards are also compatible with the Arduino IDE and support various sensors and modules.

Learn more about the STM32 Nucleo: https://www.st.com/en/evaluation-tools/stm32-nucleo-boards.html

The Nordic Semiconductor nRF52840 is a TinyML board that features a 32-bit ARM Cortex-M4 processor with a floating-point unit (FPU) and Bluetooth 5.2 support. It’s suitable for running TensorFlow Lite for Microcontrollers and can be used for a wide range of applications, from wearables to smart home devices.

The nRF52840 also includes various sensors and interfaces, making it easy to connect to other devices and peripherals. It’s compatible with the Arduino IDE and Nordic Semiconductor’s nRF5 SDK, allowing you to develop and deploy your applications quickly and easily.

Learn more about the Nordic Semiconductor nRF52840: https://www.seeedstudio.com/Seeed-XIAO-BLE-Sense-nRF52840-p-5253.html

The BeagleBone AI is a powerful TinyML board that’s built around the Texas Instruments AM5729 SoC. This SoC features dual ARM Cortex-A15 cores and dual TI C66x DSP cores, making it ideal for edge AI applications.

The BeagleBone AI also includes the TI Deep Learning (TIDL) library, which allows you to run machine learning models on the device. It’s compatible with various sensors and modules and supports various programming languages, including Python and C++.

Learn more about the BeagleBone AI: https://beagleboard.org/ai

The NVIDIA Jetson Nano is a popular choice for edge AI applications due to its powerful GPU and support for various machine learning frameworks. It features a quad-core ARM Cortex-A57 processor and a 128-core NVIDIA Maxwell GPU, making it ideal for running complex machine learning models.

The Jetson Nano also includes various sensors and interfaces, making it easy to connect to other devices and peripherals. It supports various machine learning frameworks, including TensorFlow, PyTorch, and Caffe.

Learn more about the NVIDIA Jetson Nano: https://developer.nvidia.com/embedded/jetson-nano-developer-kit

The Sipeed Maixduino is a TinyML board that features the Kendryte K210 microcontroller, which has a dual-core RISC-V processor and a built-in neural network accelerator. It’s suitable for running TensorFlow Lite for Microcontrollers and can be used for various machine learning applications.

The Maixduino also includes various sensors and interfaces, making it easy to connect to other devices and peripherals. It’s compatible with the Arduino IDE and supports various programming languages, including MicroPython and C++.

Learn more about the Sipeed Maixduino: https://s.click.aliexpress.com/e/_DBd6fTF

These are some of the most popular TinyML boards available today, each with its own unique features and capabilities. Whether you’re a developer, hobbyist, or professional, there’s a TinyML board that’s right for your project.