Thinking Machines: Machine Learning and Its Hardware Implementation

Thinking Machines: Machine Learning and Its Hardware Implementation is a book that focuses on machine learning accelerators and hardware development for machine learning. It covers the theory and application of machine learning, neuromorphic computing, and neural networks, and presents the latest trends and examples of machine learning hardware.

Format: Paperback / softback
Length: 322 pages
Publication date: 07 April 2021
Publisher: Elsevier Science Publishing Co Inc

Thinking Machines: Machine Learning and Its Hardware Implementation delves into the realm of machine learning, neuromorphic computing, and neural networks, offering a comprehensive exploration of both theory and practical applications. Serving as the inaugural text dedicated to machine learning accelerators and hardware development for machine learning, this book provides a comprehensive overview of the latest trends, examples, and fundamental knowledge in the field. It not only summarizes the state-of-the-art developments in machine learning hardware but also delves into the critical challenges involved in its implementation. By presenting a holistic perspective, readers will gain a deep understanding of the requirements necessary for designing machine learning hardware specifically tailored for neuromorphic computing and/or neural networks. Whether you have a foundational understanding of machine learning or are eager to delve deeper into the current trends of this rapidly evolving field, Thinking Machines: Machine Learning and Its Hardware Implementation is an essential resource. It serves as a valuable guide for researchers, engineers, and practitioners seeking to advance their knowledge and expertise in this domain.

Weight: 518g
Dimension: 152 x 227 x 19 (mm)
ISBN-13: 9780128182796