• Media type: E-Book
  • Title: Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design
  • Contributor: Zheng, Nan [VerfasserIn]; Mazumder, Pinaki [VerfasserIn]
  • imprint: Hoboken, NJ: Wiley, IEEE Press, [2019]
  • Extent: 1 Online-Ressource (296 Seiten)
  • Language: English
  • ISBN: 9781119507406; 9781119507390; 9781119507369
  • RVK notation: ST 601 : Einzelne Systeme (alphabetisch)
  • Keywords: Neural networks (Computer science) ; COMPUTERS / Neural Networks
  • Origination:
  • Footnote: Includes bibliographical references and index
  • Description: 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"--