Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design /
Nan Zheng, Pinaki Mazumder.
- 1 PDF (296 pages).
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.
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"--