• Medientyp: Buch
  • Titel: Learning in energy-efficient neuromorphic computing : algorithm and architecture co-design
  • Beteiligte: Zheng, Nan [VerfasserIn]; Mazumder, Pinaki [VerfasserIn]
  • Erschienen: Hoboken, NJ: Wiley, IEEE Press, 2020
  • Ausgabe: First published
  • Umfang: xx, 276 Seiten; Diagramme
  • Sprache: Englisch
  • ISBN: 9781119507383
  • RVK-Notation: ST 601 : Einzelne Systeme (alphabetisch)
  • Schlagwörter: Neural networks (Computer science) ; COMPUTERS / Neural Networks
  • Entstehung:
  • Anmerkungen: Includes bibliographical references and index
  • Beschreibung: 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"--

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  • Signatur: 2020 8 019048
  • Barcode: 12046954N