Machine Learning Development Boards

My name is Maxwell Clarke, I am an embedded systems engineer at Aaron Clarke where I am researching machine learning application development.  An easy way to get started with machine learning development is to use an off-the-shelf development board. Here are some development boards I've identified that support machine learning. I will be posting tutorials for some of these boards in the upcoming weeks.

Arduino Tiny Machine Learning Kit 

This kit's main purpose is to run TinyML projects by using TensorFLowLite on the board included.  It contains an Arduino Nano 33 BLE Sense board, which features the 32-bit Arm Cortex-M4 microcontroller in an nRF52840 processor from Nordic Semiconductors. The board also has sensors for proximity, motion, and many other measurements.  The kit costs $60 from the Arduino Store.

BeagleBone® AI-64 

The BeagleBone AI-64 has great computing power as it contains a Texas Instruments TDA4VM SoC with a dual 64-Bit Arm Cortex-A72 microprocessor. It interfaces via a M.2E-key PCIe USB 3.0 connector for WiFi, Ethernet, and USB 3.0. Also included are 4-Lane CSI and DSI connectors for cameras and displays, respectively. It is available for about $190 from online distributors such as Digi-Key. 

BeagleY-AI 

This board uses a TI AM67A Arm- based vision processor, which contains a 64-bit Arm Cortex-A53 Microcontroller. It also has a BM3301 BLE module for networking over BLE.  Other features include 3 displays including touchscreen support, a 10-pin Tag-Connect and 4 USB3 ports.  It is less expensive than the AI-64 as it has a cost of around $70.

NVIDIA Jetson Orin Nano Developer Kit

This kit contains the Jetson Orin Nano 8GB, which features an Ampere GPU and a 6-core ARM CPU. It has a processing ability of up to 40 TOPS, making it the most powerful board on this list.  Due to its top performance capabilities, it cost $500 from online distributors such as SparkFun.

Renesas Microcontrollers & Microprocessors (MCUs, MPUs) 

Renesas currently has a Reality AI Software which is useful for all sorts of ML applications. They also have a wide range of microcontrollers which are compatible with their software.  For example, there are RH850 Automotive MCUs for automotive applications, and RL78 Low Power 8 & 16-bit MCUs |which are good for getting started. Many of the microcontrollers such as the R7F7015813AFP-C#AA3 , which has a cost of $17, can be purchased from online distributors. 

STM32 Microcontrollers (MCUs)

Currently, St Microelectronics has a vast library of Edge AI tools including MEMS Studio.  These software libraries can be used for developing ML applications with STM32 boards.  For example, the X-NUCLEO-IKS4A1 is a sensor kit forSTM32 boards that can be used with MEMS Studio. Both the kit and development boards can be purchased directly from STMicro.  The X-NUCLEO-IKS4A1 costs about $33, and a new compatible board, the NUCLEO-G491RE costs about $16.

    

Edge Impulse 

Edge Impulse is a suite of tools for conducting machine learning on edge devices.  It has different ways to get started for beginners, ML practitioners, and embedded engineers. The getting started for beginners is a good way to begin with machine learning, without much coding required.