Code: USD75OFF
This is the code repository for TinyML Cookbook, published by Packt.
Author: Gian Marco Iodice
Publisher: Packt
This book is about 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.
TinyML is an exciting field full of opportunities. With a small budget, we can give life to objects that interact with the world around us smartly and transform the way we live for the better. However, this field can be hard to approach if we come from an ML background with a little familiarity with embedded systems such as microcontrollers. Therefore, this book wants to dispel these barriers and make TinyML also accessible to developers with no embedded programming experience through practical examples. Each chapter will be a self-contained project to learn how to use some of the technologies at the heart of TinyML, interface with electronic components like sensors, and deploy ML models on memory-constrained devices.
This book is for ML developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. The book will help you expand your knowledge towards the revolution of tiny machine learning (TinyML) by building end-to-end smart projects with real-world data sensors on Arduino Nano 33 BLE Sense and Raspberry Pi Pico.
Basic familiarity with C/C++, Python programming, and a command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.
You will need a computer (either a laptop or desktop) with an x86-64 architecture and at least one USB port for programming Arduino Nano 33 BLE Sense and Raspberry Pi Pico microcontroller boards. For the first six chapters, you can use Ubuntu 18.04 (or later) or Windows (for example, Windows 10) as an operating system (OS). However, you will need Ubuntu 18.04 (or later) for chapter 7 and chapter 8.
The only software prerequisites for your computer are:
- Python (Python 3.7 recommended)
- Text editor (for example, gedit on Ubuntu)
- Media player (for example, VLC)
- Image viewer (for example, the default app in Ubuntu or Windows 10)
- Web browser (for example, Google Chrome)
Arduino Nano 33 BLE Sense and Raspberry Pi Pico programs will be developed directly in the web browser with the Arduino Web Editor. However, you may also consider using the local Arduino IDE following the instructions provided at this link.
The following table summarizes the hardware devices and software tools covered in each chapter:
| Chapter | Devices | SW tools | Electronic components |
|---|---|---|---|
| 1 | - Arduino Nano 33 BLE Sense - Raspberry Pi Pico |
- Arduino Web Editor | None |
| 2 | - Arduino Nano 33 BLE Sense - Raspberry Pi Pico |
- Arduino Web Editor | - A micro-USB cable - 1x half-size breadboard - 1x red LED - 1x 220 Ohm resistor - 1x 3 AA battery holder - 1x 4 AA battery holder - 4x AA batteries - 5x jumper wires |
| 3 | - Arduino Nano 33 BLE Sense - Raspberry Pi Pico |
- Arduino Web Editor - Google Colaboratory |
- A micro-USB cable - 1x half-size breadboard - 1x AM2302 module with the DHT22 sensor - 5x jumper wires |
| 4 | - Arduino Nano 33 BLE Sense - Raspberry Pi Pico |
- Arduino Web Editor - Edge Impulse - Python |
- A micro-USB cable - 1x half-size breadboard - 1x electrect microphone amplifier - MAX9814 - 2x 220 Ohm resistor - 1x 100 Ohm resistor - 1x red LED - 1x green LED - 1x blue LED - 1x push-button - 11x jumper wires |
| 5 | - Arduino Nano 33 BLE Sense | - Arduino Web Editor - Google Colaboratory - Python |
- A micro-USB cable - 1x half-size breadboard - 1x OV7670 camera module - 1x push-button - 18 jumper wires |
| 6 | - Raspberry Pi Pico | - Arduino Web Editor - Edge Impulse - Python |
- A micro-USB cable - 1x half-size breadboard - 1x MPU-6050 IMU - 4x jumper wires |
| 7 | - Arm Cortex-M3 Virtual Platform (QEMU) | - Google Colaboratory - Python - Zephyr project |
None |
| 8 | - Virtual Arm Ethos-U55 microNPU | - Arm Corstone-300 FVP - Python - TVM |
None |
To cite TinyML Cookbook in publications use:
@book{iodice2022tinymlcookbook,
title={TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter},
author={Gian Marco Iodice},
year={2022},
publisher={Packt},
isbn = {9781801814973},
url = {https://www.packtpub.com/product/tinyml-cookbook/9781801814973}
}
Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. 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 co-founded the TinyML UK meetup group to encourage knowledge sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.