Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design / Nan Zheng, Pinaki Mazumder.
By: Zheng, Nan [author.].
Contributor(s): Mazumder, Pinaki [author.] | IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type:
BookPublisher: Hoboken, New Jersey : Wiley-IEEE Press, [2019]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2019]Description: 1 PDF (296 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119507369.Subject(s): Neural networks (Computer science)Genre/Form: Electronic books.DDC classification: 006.3/2 Online resources: Abstract with links to resource Also available in print.Includes bibliographical references and index.
Overview -- Fundamentals and learning of artificial neural networks -- Artificial neural networks in hardware -- Operational principles and learning in SNNs -- Hardware implementations of spiking neural networks.
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"This book focuses on how to build energy-efficient hardware for neural network with learning capabilities. One of the striking features of this book is that it strives to provide a co-design and co-optimization methodologies for building hardware neural networks that can learn. The book provides a complete picture from high-level algorithm to low-level implementation details. The book also covers many fundamentals and essentials in neural networks, e.g., deep learning, as well as hardware implementation of neural networks. This book will serve as a good resource for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities"-- Provided by publisher.
Also available in print.
Mode of access: World Wide Web
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