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TinyML Cookbook

You're reading from TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801814973

Length 344 pages

Edition 1st Edition

Tools

Combine

Concepts

Artificial Intelligence

Author (1):

Gian Marco Iodice

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Getting Started with TinyML

2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER

3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Chapter 4: Voice Controlling LEDs with Edge Impulse

5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano

6. Chapter 6: Building a Gesture-Based Interface for YouTube Playback

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

8. Chapter 8: Toward the Next TinyML Generation with microNPU

9. Other Books You May Enjoy

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...

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Authors (1)

Gian Marco Iodice

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.

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