Tiny Machine Learning: Design Principles and Applications - Hardcover

Über die Autorin bzw. den Autor

Agbotiname Lucky 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.

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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.