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

By:

Zheng, Nan, 1989- [author.]

Contributor(s):

Material type: Text

TextPublisher: 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 resource

ISBN:

9781119507369

Subject(s):

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:

Abstract with links to resource

Also available in print.

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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

No physical items for this record

Includes bibliographical references and index.

Restricted to subscribers or individual electronic text purchasers.

"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.