VLSI for Neural Networks and Artificial Intelligence

  • José G. Delgado-Frias
  • William R. Moore

Table of contents

  1. Front Matter
    Pages i-x
  2. Analog Circuits for Neural Networks

    1. Maurizio Valle, Daniele D. Caviglia, Giacomo M. Bisio
      Pages 35-44
    2. Rüdiger W. Brause
      Pages 53-60
    3. Luigi Raffo, Giacomo M. Bisio, Daniele D. Caviglia, Giacomo Indiveri, Silvio P. Sabatini
      Pages 61-70
  3. Digital Implementations of Neural Networks

    1. José G. Delgado-Frias, Stamatis Vassiliadis, Gerald G. Pechanek, Wei Lin, Steven M. Barber, Hui Ding
      Pages 71-80
    2. William Fornaciari, Fabio Salice
      Pages 81-91
    3. Marc A. Viredaz, Christian Lehmann, François Blayo, Paolo Ienne
      Pages 93-102
    4. Krste Asanović, James Beck, Brian E. D. Kingsbury, Phil Kohn, Nelson Morgan, John Wawrzynek
      Pages 103-107
    5. Tadashi Ae, Reiji Aibara, Kazumasa Kioi
      Pages 109-117
    6. Terence Hui, Paul Morgan, Hamid Bolouri, Kevin Gurney
      Pages 119-127
    7. John F. Hurdle, Erik L. Brunvand, Lüli Josephson
      Pages 129-139
  4. Neural Networks on Multiprocessor Systems and Applications

    1. Thomas F. Ryan, José G. Delgado-Frias, Stamatis Vassiliadis, Gerald G. Pechanek, Douglas M. Green
      Pages 151-158
    2. Jean-Dominique Gascuel, Eric Delaunay, Lionel Montoliu, Bahram Moobed, Michel Weinfeld
      Pages 159-166
    3. U. Rückert, S. Rüping, E. Naroska
      Pages 167-176
    4. Chang J. Wang, Edward P. K. Tsang
      Pages 187-196

About this book


Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.


CMOS VLSI artificial intelligence backpropagation knowledge learning neural network

Editors and affiliations

  • José G. Delgado-Frias
    • 1
  • William R. Moore
    • 2
  1. 1.State University of New York at BinghamtonBinghamtonUSA
  2. 2.Oxford UniversityOxfordUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4899-1331-9
  • Copyright Information Springer-Verlag US 1994
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4899-1333-3
  • Online ISBN 978-1-4899-1331-9
  • About this book