About this ebook
TinyML (tiny machine learning), short for tiny machine learning, represents a groundbreaking intersection of machine learning and embedded systems, enabling the deployment of intelligent applications on resource-constrained devices. It empowers these devices to perform complex tasks, like image and speech recognition, locally without relying on cloud servers. This burgeoning field opens up many possibilities, from enhancing IoT devices to revolutionizing healthcare and intelligent infrastructure. As technology advances, TinyML promises to make our everyday devices more innovative, responsive, and efficient than ever before. By bringing inference to resource-constrained hardware, TinyML supports real-time decision-making while addressing critical concerns such as latency, power consumption, and data privacy. This book presents an overview of TinyML, including its core principles, applications, challenges, and future directions. It meticulously explores the fundamentals of machine learning and deep learning, providing a solid foundation for understanding how these techniques are adapted for tiny devices. By delving into the hardware, software, and algorithms that specifically cater to TinyML, the book addresses the unique challenges of running machine-learning models on devices with limited processing power and memory. Featuring expert insights and real-world case studies, this volume is an essential guide to researchers and industry professionals looking for solutions for today’s resource-constrained devices.
Readers will find the volume:
- Delves into the burgeoning field of TinyML, where the power of machine learning is harnessed for resource-constrained devices;
- Serves as a comprehensive guide, equipping readers with the essential knowledge to develop and deploy TinyML applications;
- Explores the fundamentals of machine learning and deep learning, providing a solid foundation for understanding how these techniques are adapted for tiny devices;
- Introduces the hardware, software, and algorithms that specifically cater to TinyML, addressing the unique challenges of running machine-learning models on devices with limited processing power and memory.
Audience
Engineers, academics, researchers, and professionals in computer science, information technology, and electronics and communication.
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About the author
Rajdeep Chakraborty, PhD is a Professor in the Department of Computer Science and Engineering, Institute of Engineering and Technology, SAGE University, Indore, Madhya Pradesh, India with more than two decades of teaching experience and extensive involvement in research. He has made notable contributions through various publications, including patents, books, journal articles, and conference papers. His research interests include cryptography, network security, IoT, and blockchain.
Rana Majumdar, PhD is an Associate Professor at the Sister Nivedita University, Kolkata, West Bengal, India. He is the author of numerous research publications at the national and international levels, three books, one copyright, and 12 patents. His research focuses on machine learning, computer vision, software reliability engineering, digital image and video processing, and machine learning.
S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published more than 90 books, 300 articles in national and international journals and conferences, and 300 patents. He is a research consultant for many companies, startups, and micro-, small, and medium enterprises.
Sheng-Lung Peng, PhD is a Professor in the Department of Creative Technologies and Product Design and the Dean of the College of Innovative Design and Management at the National Taipei University of Business, Taiwan. In addition to his roles at NTUB, he holds honorary and adjunct professorships at several institutions and serves as the President of the Association of Taiwan Computer Programming Contest and the Association of Algorithms and Computation Theory. His research focuses on designing algorithms in artificial intelligence, bioinformatics, combinatorics, data mining, and networking, with more than 100 research papers published in these areas.
