You're reading from TinyML Cookbook - Second Edition

Product typeBook

Published inNov 2023

PublisherPackt

ISBN-139781837637362

Edition2nd Edition

Concepts

Machine Learning

Author (1)

Gian Marco Iodice

Introduction to the development platforms

The development platforms used in this book are microcontroller boards. A microcontroller board is a printed circuit board (PCB) that combines a microcontroller with the necessary electronic circuit to make it ready for use. In some cases, these platforms could also include additional devices, such as sensors or additional external memory, to target specific end applications.

The Arduino Nano 33 BLE Sense (Arduino Nano for short), Raspberry Pi Pico, and the SparkFun RedBoard Artemis Nano (SparkFun Artemis Nano for short) are the microcontroller boards used in this book.

As we will see in more detail in the upcoming subsections, the platforms have an incredibly small form factor, a USB port for power/programming, and an Arm-based microcontroller. At the same time, they also have unique features that make them ideal for targeting different development scenarios.

Arduino Nano 33 BLE Sense

The Arduino Nano, designed by Arduino (https://www.arduino.cc), is a versatile platform suitable for various tinyML applications. It integrates the nRF52840 microcontroller, powered by an Arm Cortex-M4 CPU that runs at 64 MHz, as well as 1 MB of program memory and 256 KB of data memory, along with various sensors and a Bluetooth radio:

Diagram

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Figure 1.19: Arduino Nano board

When developing on the Arduino Nano, we only need to add a few additional external components, as most are already on-board.

The Arduino Nano 33 BLE Sense underwent an upgrade to the Rev2 version in 2023. This updated version retains the same form factor and processor as the Rev1 but includes enhanced sensors to cover a broader range of applications. The projects featured in this book are compatible with both the Rev1 and Rev2 versions.

Raspberry Pi Pico

Raspberry Pi Pico, designed by Raspberry Pi (https://www.raspberrypi.org), does not provide sensors and the Bluetooth module on-board. Still, it has the RP2040 microcontroller powered by a dual-core Arm Cortex-M0+ processor, running at 13 3MHz with 264 KB of SRAM. The device boasts an external flash memory of 2 MB for the program, making it an excellent choice for tinyML applications that require speed and memory space:

A computer parts and information

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Figure 1.20: Raspberry Pi Pico board

In this book, this board will be ideal for learning how to interface with external sensors and build electronic circuits.

SparkFun RedBoard Artemis Nano

The SparkFun RedBoard Artemis Nano, designed by SparkFun Electronics (https://www.sparkfun.com/), is a platform that integrates the Apollo3 microcontroller, powered by an Arm Cortex-M4F processor running at 48 MHz with 1 MB of program memory and 384 KB of data memory.

The platform also boosts a digital microphone, making it ideal for those interested in developing always-on voice command applications:

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Figure 1.21: SparkFun RedBoard Artemis Nano

This platform is optional but recommended to grasp the concepts presented in the recipes for the Arduino Nano and Raspberry Pi Pico, using an alternative device.

This book will not include a comprehensive discussion about projects for the SparkFun RedBoard Artemis Nano. However, when you come across the There’s more…with the SparkFun Artemis Nano! section at the end of a recipe, you can find the instructions to replicate it on this device.

Although the book will not discuss projects for the SparkFun RedBoard Artemis Nano, the source code for this platform will be accessible on GitHub.

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Author (1)

Gian Marco Iodice

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
Read more about Gian Marco Iodice

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