Learning in energy-efficient neuromorphic computing :
General Material Designation
[Book]
Other Title Information
algorithm and architecture co-design /
First Statement of Responsibility
Nan Zheng, Pinaki Mazumder.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Hoboken, NJ :
Name of Publisher, Distributor, etc.
Wiley-IEEE Press,
Date of Publication, Distribution, etc.
2020.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xx, 276 pages)
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
Overview -- Fundamentals and learning of artificial neural networks -- Artificial neural networks in hardware -- Operational principles and learning in spiking neural networks -- Hardware implementations of spiking neural networks.
0
SUMMARY OR ABSTRACT
Text of Note
"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"--
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
OverDrive, Inc.
Stock Number
29444C11-7284-4501-AED9-4C422116AA49
OTHER EDITION IN ANOTHER MEDIUM
Title
Learning in energy-efficient neuromorphic computing