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Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design / Nan Zheng, Pinaki Mazumder.

By: Zheng, Nan, 1989- [author.].
Contributor(s): Mazumder, Pinaki [author.].
Material type: materialTypeLabelBookPublisher: Hoboken, NJ : Wiley-IEEE Press, [2019]Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781119507369; 1119507367.Subject(s): Neural networks (Computer science) | Neural networks (Computer science)Genre/Form: Electronic books.Additional physical formats: Print version:: Learning in energy-efficient neuromorphic computing.DDC classification: 006.3/2 Online resources: Wiley Online Library
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.
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 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.
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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.

"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.

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