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)

Building the circuit with the Raspberry Pi Pico to voice control LEDs

The Raspberry Pi Pico has neither a microphone nor RGB LEDs onboard for building a KWS application. Therefore, voice controlling the RGB LEDs on this platform requires building an electronic circuit.

This recipe aims to prepare a circuit with the Raspberry Pi Pico, RGB LEDs, a push-button, and an electret microphone with a MAX9814 amplifier.

Getting ready

The application we have considered for the Raspberry Pi Pico is not based on continuous inferencing. Here, we would like to use a button to start the audio recording of 1 s and then run the model inference to recognize the utterance. The spoken word, in turn, will be used to control the status of the RGB LEDs.

In the following subsection, we will learn more about using the electret microphone with the MAX9814 amplifier.

Introducing the electret microphone amplifier with the MAX9814 amplifier

The microphone put into action in this recipe is the...