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)

Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS

Prototyping a TinyML application directly on a physical device is really fun because we can instantly see our ideas at work in something that looks and feels like the real thing. However, before any application comes to life, we need to ensure that the models work as expected and, possibly, among different devices. Testing and debugging applications directly on microcontroller boards often requires a lot of development time. The main reason for this is the necessity to upload a program into a device for every change in code. However, virtual platforms can come in handy to make testing more straightforward and faster.

In this chapter, we will build an image classification application with TensorFlow Lite for Microcontrollers (TFLu) for an emulated Arm Cortex-M3 microcontroller. We will start by installing the Zephyr OS, the primary framework used in this chapter to accomplish our task. Next, we will...