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.]
Language: English Publisher: Hoboken, New JerseyJ : Wiley-IEEE Press, 2020Description: 1 online resource (296 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119507406; 9781119507390; 9781119507369Subject(s): Neural networks (Computer science)Genre/Form: Electronic booksAdditional physical formats: Print version:: Learning in energy-efficient neuromorphic computingDDC classification: 006.3/2 LOC classification: QA76.87Online resources: Full text available at Wiley Online Library Click here to view
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.

Description based on print version record and CIP data provided by publisher; resource not viewed.

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