Book Image

TinyML Cookbook

By : Gian Marco Iodice
Book Image

TinyML Cookbook

By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)

Introducing TinyML

Throughout all the recipes presented in this book, we will give practical solutions for tiny machine learning, or, as we will refer to it, TinyML. In this section, we will learn what TinyML is and the vast opportunities it brings.

What is TinyML?

TinyML is the set of technologies in ML and embedded systems to make use of smart applications on extremely low-power devices. Generally, these devices have limited memory and computational capabilities, but they can sense the physical environment through sensors and act based on the decisions taken by ML algorithms.

In TinyML, ML and the deployment platform are not just two independent entities but rather entities that need to know each other at best. In fact, designing an ML architecture without considering the target device characteristics will make it challenging to deploy effective and working TinyML applications.

On the other hand, it would be impossible to design power-efficient processors to expand the...