Tiny Machine Learning: Design Principles and Applications

1. Auflage 2025
ISBN: 978-1-394-29454-1
Verlag: Wiley

Buch, Englisch, 400 Seiten

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development

In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design.

Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications.

Additional topics covered in the book include: - A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes
- Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML
- Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis

Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Autoren/Hrsg.

Imoize, Agbotiname Lucky

Do, Dinh-Thuan

Song, Houbing

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie

Inhaltsverzeichnis

Chapter 1  Introduction to TinyML

Francisca Onyiyechi Nwokoma, Chidi Ukamaka Betrand, Juliet Nnenna Odii, Euphemia Chioma Nwokorie, and Euphemia Chioma Nwokorie

Chapter 2  Learning Panorama Under TinyML

Ikechukwu Ignatius Ayogu, Euphemia Chioma Nwokorie, Juliet Nnenna Odii, Francisca Onyiyechi Nwokoma, and Chidi Ukamaka Betrand

Chapter 3 TinyML for Anomaly Detection

Richard Govada Joshua, Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, and Samuel Oluwatobi Tofade

Chapter 4 TinyML Power Consumption and Memory in IoT MCUs

Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, Richard Govada Joshua, and Samuel Oluwatobi Tofade

Chapter 5 Efficient Data Cleaning and Anomaly Detection in IoT Devices Using TinyCleanEDF

Ilker Kara

Chapter 6 TinyML devices and tools

Abeeb Akorede Bello, Agbotiname Lucky Imoize, and Agbotiname Lucky Imoize

Chapter 7 Privacy-Preserving Techniques in TinyML for IoT

Oleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Marco Arnesano, and Pavlo Usik

Chapter 8 Enhancing Cybersecurity in TinyML with Lightweight Cryptographic Algorithms

Oleksandr Kuznetsov, Roman Minailenko, and Aigul Shaikhanova

Chapter 9 Tiny Machine Learning for Enhanced Edge Intelligence

Emmanuel Alozie, Agbotiname Lucky Imoize, Hawau I. Olagunju, Nasir Faruk, Salisu Garba, and Ayobami P. Olatunji

Über Autor(innen)

Agbotiname Imoize is a Lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. He is a Fulbright Fellow, the Vice Chair of the IEEE Communication Society Nigeria chapter, and a Senior Member of IEEE.

Dinh-Thuan Do, PhD, is an Assistant Professor with the School of Engineering at the University of Mount Union, USA. He is an editor of IEEE Transactions on Vehicular Technology and Computer Communications. He is a Senior Member of IEEE.

Houbing Herbert Song, PhD, IEEE Fellow, is a Professor in the Department of Information Systems, and the Department of Computer Science and Electrical Engineering and Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab) at the University of Maryland, Baltimore County. He is also Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics.