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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

Chapter 4: Voice Controlling LEDs with Edge Impulse

Keyword spotting (KWS) is a technology applied in a wide range of daily-life applications to enable an entirely hands-free experience with the device. The detection of the famous wake-up words OK Google, Alexa, Hey Siri, or Cortana represents a particular usage of this technology, where the smart assistant continuously listens for the magic phrase before starting to interact with the device.

Since KWS aims to identify utterances from real-time speech, it needs to be on-device, always-on, and running on a low-power system to be effective.

This chapter demonstrates the usage of KWS through Edge Impulse by building an application to voice control the light-emitting diode (LED)-emitting color (red, green, and blue (or RGB)) and the number of times to make it blink (one, two, and three times).

This TinyML application could find space in smart educational toys to learn both color and number vocabulary with peace of mind regarding...

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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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