Tiny Machine Learning: Next-Generation Artificial Intelligence

This comprehensive guide provides the foundational tools, optimization techniques, and practical case studies you need to engineer autonomous edge systems across healthcare, industrial automation, and IoT.

Deploying machine learning algorithms on devices with limited resources, such as microcontrollers and low-power edge devices, is known as TinyML (tiny machine learning). These devices are typically limited by memory, power, and processing capabilities, yet TinyML allows real-time processing and decision-making at the edge without relying on cloud computing. Despite these challenges, TinyML is becoming essential for edge applications that require fast, efficient, and autonomous operations.

TinyML is revolutionizing industries by enabling sophisticated machine learning capabilities on hardware with limited resources. As edge computing and the Internet of Things continue to expand, TinyML forms the backbone of smarter, more efficient applications in sectors such as healthcare, industrial automation, and environmental monitoring.

This book provides a comprehensive guide to this rapidly evolving field. It presents the concepts, methods, algorithms, and tools of TinyML, covering foundational principles, hardware and software platforms, optimization techniques, and real-world case studies. The book is tailored for practitioners, researchers, and enthusiasts eager to understand and leverage the power of TinyML.

Someet Singh, PhD, is an Associate Professor and Coordinator of Academic Operations at Lovely Professional University, Phagwara, Punjab. His contributions include multiple patents and book chapters and more than 17 research publications in prestigious journals and conferences. He specializes in the Internet of Things, mechatronics, and machine learning.

Deepika Ghai, PhD, is an Assistant Professor at Lovely Professional University with more than seven years of experience in academia. She has published more than 50 research papers in refereed journals and conferences, as well as a number of edited books. Her areas of expertise include signal and image processing, biomedical signal and image processing, and VLSI signal processing.

Suman Lata Tripathi, PhD, is a Professor at Lovely Professional University with more than 22 years of experience in academia and research. She has published more than 152 research papers in refereed journals, conference proceedings, and e-books, as well as more than 30 books, 20 Indian patents, and four copyrights. Her research focuses on microelectronics device modeling and characterization, low-power VLSI circuit design, and VLSI design testing.

Navjot Kaur is an Assistant Professor in the Department of System Programming at Lovely Professional University, Phagwara. With more than 15 years of experience in academia and industry, she specializes in image processing, system programming, and advanced computational methodologies. Her professional career spans roles as a software developer, lecturer, Head of Department, and mentor, guiding numerous capstone projects and theses.