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Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design

Author: Nan Zheng; Pinaki Mazumder
Publisher: Hoboken, NJ : Wiley-IEEE Press, [2019]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Zheng, Nan, 1989-
Learning in energy-efficient neuromorphic computing
Hoboken, NJ : Wiley-IEEE Press, [2019]
(DLC) 2019029946
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nan Zheng; Pinaki Mazumder
ISBN: 9781119507406 1119507405 9781119507390 1119507391 9781119507369 1119507367
OCLC Number: 1119123494
Description: 1 online resource
Contents: 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.
Responsibility: Nan Zheng, Pinaki Mazumder.

Abstract:

"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"--

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