TinyML boards are small, low-power microcontrollers that are designed to run machine learning models at the edge. These boards are a great way to bring machine learning capabilities to your projects, and they are becoming increasingly popular as more and more people discover their potential.

Introduction

In this post, we’ll take a look at some of the most popular TinyML boards currently available. We’ll provide a brief overview of each board, as well as a link where you can find more information and purchase them.

Arduino Nano 33 BLE Sense

The Arduino Nano 33 BLE Sense is a small, powerful microcontroller that is perfect for TinyML projects. The board uses mainly the Arduino Integrated Development Environment (IDE) and has a range of libraries and example projects available.

• 9 axis inertial sensor: what makes this board ideal for wearable devices
• Humidity, and temperature sensor: to get highly accurate measurements of the environmental conditions
• Barometric sensor: you could make a simple weather station
• Microphone: to capture and analyse sound in real time
• Gesture, proximity, light color and light intensity sensor : estimate the room’s luminosity, but also whether someone is moving close to the board

You can find more information and purchase the Arduino Nano 33 BLE Sense here: https://store.arduino.cc/products/arduino-nano-33-ble-sense?selectedStore=eu

Adafruit EdgeBadge

The Adafruit EdgeBadge is a tiny, low-power microcontroller that is specifically designed for TinyML projects.

• ATSAMD51J19 @ 120MHz with 3.3V logic/power – 512KB of FLASH + 192KB of RAM
• 2 MB of SPI Flash for storing images, sounds, animations, whatever!
• 1.8″ 160×128 Color TFT Display connected to its own SPI port
• 8 x Game/Control Buttons with nice silicone button tops (these feel great)
• 5 x NeoPixels for badge dazzle, or game score-keeping
• Triple-axis accelerometer (motion sensor)
• Light sensor, reverse-mount so that it points out the front
• Built in buzzer mini-speaker
• Mono Class-D speaker driver for 4-8 ohm speakers, up to 2 Watts
• LiPoly battery port with built in recharging capability
• USB port for battery charging, programming and debugging
• Two female header strips with Feather-compatible pinout so you can plug any FeatherWings in
• JST ports for NeoPixels, sensor input, and I2C (you can fit I2C Grove connectors in here)
• Reset button
• On-Off switch

You can find more information and purchase the Adafruit EdgeBadge here: https://www.adafruit.com/product/4500

SparkFun Edge

The SparkFun Edge is a powerful, low-power microcontroller that is perfect for TinyML projects. It has a Cortex M4 processor and comes with a number of built-in sensors.

Microcontroller

• 32-bit ARM Cortex-M4F processor with Direct Memory Access
• 48MHz CPU clock, 96MHz with TurboSPOT™
• Extremely low-power usage: 6uA/MHz
• 1MB Flash
• 384KB SRAM
• Dedicated Bluetooth processor with BLE 5

Onboard

• ST LIS2DH12 3-axis accelerometer
• 2x MEMS microphones with operational amplifier
• Himax HM01B0 camera connector
• Qwiic connector
• 4 x GPIO connections
• 4 x user LEDs
• 1 x user button
• FTDI-style serial header for programming
• Bluetooth antenna
• CR2032 coin cell holder for battery operation

You can find more information and purchase the SparkFun Edge here: https://www.sparkfun.com/products/15170

Coral Dev Board

The Coral Dev Board is a powerful, low-power microcontroller that is specifically designed for machine learning projects. The Dev Board also has a range of libraries and example projects available, so you can easily build and deploy machine learning models at the edge.

You can find more information and purchase the Coral Dev Board here: https://coral.ai/products/dev-board/

Seeeduino XIAO

The Seeeduino XIAO is a small, low-power microcontroller that is perfect for TinyML projects. The XIAO is compatible with the Arduino Integrated Development Environment (IDE) and has a range of libraries and example projects available.

• High Performance: Powered by SAMD21G18 chip, operating up to 48MHz, equipped with 32KB of SRAM, and 256KB of onboard flash memory
• Ultra-small Design: 21 x 17.5mm, Seeed Studio XIAO series classic form-factor, suitable for wearable devices
• Multiple Development Interfaces: 11x analog / 11x digital Pins, 1x I2C interface, 1x UART port, and 1 SPI port
• Multiple Develop Platform: Support Arduino / Micropython / CircuitPython development, friendly for beginners, satisfied for electronics enthusiasts
• Perfect for Production: Breadboard-friendly & SMD design, no components on the back

You can find more information and purchase the Seeeduino XIAO here: https://www.seeedstudio.com/Seeeduino-XIAO-Arduino-Microcontroller-SAMD21-Cortex-M0+-p-4426.html

BBC micro:bit

The BBC micro:bit is a small, low-power microcontroller that is perfect for TinyML projects. The micro:bit is easy to program and has a range of libraries and example projects available.

• Microprocessor: 32-bit ARM® Cortex™ M0 CPU
• A 5×5 LED matrix with 25 red LEDs to light up and can display animiated patterns, scrolling text and alphanumeric characters
• Two programmable buttons. Use them as a games controller, or control music on a smart phone
• On-board motion detector or 3-AXIS digital accelerometer that can detect movement e.g. shake, tilt or free-fall
• A built-in compass, 3D magnetometer to sense which direction you’re facing and your movement in degrees and detect the presence of certain metals and magnets
• Bluetooth® Smart Technology. Connect the micro:bit to other micro:bits, devices, phones, tablets, cameras and other everday objects
• 20 pin edge connector: This allows the micro:bit to be connected to other devices such as Raspberry Pi, Arduino, Galileo and Kano through a standard connector
• Micro-USB controller: This is controlled by a separate processor and presents the micro:bit to a computer as a memory stick
• Five Ring Input and Output (I/O) including power (PWR), ground (GRD) and 3 x I/O.
• System LED x 1 (yellow)

You can find more information and purchase the BBC micro:bit here: https://www.microbit.org/buy/

Teensy 4.1

The Teensy 4.1 is a powerful, low-power microcontroller that is perfect for TinyML projects. It has a Cortex M7 processor. The Teensy 4.1 is easy to program and has a range of libraries and example projects available.

• ARM Cortex-M7 at 600 MHz
• Float point math unit, 64 & 32 bits
• 7936K Flash, 1024K RAM (512K tightly coupled), 4K EEPROM (emulated)
• QSPI memory expansion, locations for 2 extra RAM or Flash chips
• USB device 480 Mbit/sec & USB host 480 Mbit/sec
• 55 digital input/output pins, 35 PWM output pins
• 18 analog input pins
• 8 serial, 3 SPI, 3 I2C ports
• 2 I2S/TDM and 1 S/PDIF digital audio port
• 3 CAN Bus (1 with CAN FD)
• 1 SDIO (4 bit) native SD Card port
• Ethernet 10/100 Mbit with DP83825 PHY
• 32 general purpose DMA channels
• Cryptographic Acceleration & Random Number Generator
• RTC for date/time
• Programmable FlexIO
• Pixel Processing Pipeline
• Peripheral cross triggering
• Power On/Off management

You can find more information and purchase the Teensy 4.1 here: https://www.pjrc.com/store/teensy41.html

Raspberry Pi Pico

The Raspberry Pi Pico is a small, low-power microcontroller that is perfect for TinyML projects. It has a Cortex M0+. The Pico is compatible with the Python programming language and has a range of libraries and example projects available.

• 21 mm × 51 mm form factor
• RP2040 microcontroller chip designed by Raspberry Pi in the UK
• Dual-core Arm Cortex-M0+ processor, flexible clock running up to 133 MHz
• 264kB on-chip SRAM
• 2MB on-board QSPI flash
• 2.4GHz 802.11n wireless LAN (Raspberry Pi Pico W and WH only)
• 26 multifunction GPIO pins, including 3 analogue inputs
• 2 × UART, 2 × SPI controllers, 2 × I2C controllers, 16 × PWM channels
• 1 × USB 1.1 controller and PHY, with host and device support
• 8 × Programmable I/O (PIO) state machines for custom peripheral support
• Supported input power 1.8–5.5V DC
• Operating temperature -20°C to +85°C (Raspberry Pi Pico and Pico H); -20°C to +70°C (Raspberry Pi Pico W and Pico WH)
• Castellated module allows soldering direct to carrier boards (Raspberry Pi Pico and Pico W only)
• Drag-and-drop programming using mass storage over USB
• Low-power sleep and dormant modes
• Accurate on-chip clock
• Temperature sensor
• Accelerated integer and floating-point libraries on-chip

You can find more information and purchase the Raspberry Pi Pico here: https://www.raspberrypi.org/products/raspberry-pi-pico/

Conclusion

These are just a few examples of tinyML boards, and there are many other options available depending on your needs and budget. Whether you’re a hobbyist, a developer, or a researcher, there is a tinyML board out there that can meet your needs and help you build innovative machine learning applications.

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