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VLSI for Artificial Intelligence and Neural Networks

  • Book
  • © 1991

Overview

Editors:
  1. José G. Delgado-Frias

    1. State University of New York at Binghamton, Binghamton, USA

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  2. William R. Moore

    1. Oxford University, Oxford, UK

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Table of contents (39 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Architecture and Hardware Support for AI Processing

    1. VLSI Design of a 3-D Highly Parallel Message-Passing Architecture

      • Jean-Luc Bechennec, Christophe Chanussot, Vincent Neri, Daniel Etiemble
      Pages 1-10

    2. Architectural Design of the Rewrite Rule Machine Ensemble

      • Hitoshi Aida, Sany Leinwand, José Meseguer
      Pages 11-22

    3. A Dataflow Architecture for AI

      • José Delgado-Frias, Ardsher Ahmed, Robert Payne
      Pages 23-32

    4. Incremental Garbage Collection Scheme in KL1 and Its Architectural Support of PIM

      • Yasunori Kimura, Takashi Chikayama, Tsuyoshi Shinogi, Atsuhiro Goto
      Pages 33-45

    5. COLIBRI: A Coprocessor for LISP based on RISC

      • Christian Hafer, Josef Plankl, Franz Josef Schmitt
      Pages 47-56

    6. A CAM Based Architecture for Production System Matching

      • Pratibha, P. Dasiewicz
      Pages 57-66

    7. SIMD Parallelism for Symbol Mapping

      • Chang Jun Wang, Simon H. Lavington
      Pages 67-78

    8. Logic Flow in Active Data

      • Peter S. Sapaty
      Pages 79-91

    9. Parallel Analogue Computation for Real-Time Path Planning

      • Lionel Tarassenko, Gillian Marshall, Felipe Gomez-Castaneda, Alan Murray
      Pages 93-99

  3. Machines for Prolog

    1. An Extended Prolog Instruction Set for RISC Processors

      • Andreas Krall
      Pages 101-108

    2. A VLSI Engine for Structured Logic Programming

      • Pierluigi Civera, Evelina Lamma, Paola Mello, Antonio Natali, Gianluca Piccinini, Maurizio Zamboni
      Pages 109-119

    3. Performance Evaluation of a VLSI Associative Unifier in a WAM Based Environment

      • P. L. Civera, G. Masera, G. L. Piccinini, M. Ruo Roch, M. Zamboni
      Pages 121-131

    4. A Parallel Incremental Architecture for Prolog Program Execution

      • Alessandro De Gloria, Paolo Faraboschi, Elio Guidetti
      Pages 133-142

    5. An Architectural Characterization of Prolog Execution

      • Mark A. Friedman, Gurindar S. Sohi
      Pages 143-152

    6. A Prolog Abstract Machine for Content-Addressable Memory

      • Hamid Bacha
      Pages 153-164

    7. A Multi-Transputer Architecture for a Parallel Logic Machine

      • Mario Cannataro, Giandomenico Spezzano, Domenico Talia
      Pages 165-174

  4. Analogue and Pulse Stream Neural Networks

    1. Computational Capabilities of Biologically-Realistic Analog Processing Elements

      • Chris Fields, Mark DeYong, Randall Findley
      Pages 175-184

    2. Analog VLSI Models of Mean Field Networks

      • Christian Schneider, Howard Card
      Pages 185-194

    3. An Analogue Neuron Suitable for a Data Frame Architecture

      • W. A. J. Waller, D. L. Bisset, P. M. Daniell
      Pages 195-204
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Keywords

About this book

This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.

Editors and Affiliations

  • State University of New York at Binghamton, Binghamton, USA

    José G. Delgado-Frias

  • Oxford University, Oxford, UK

    William R. Moore

Bibliographic Information

  • Book Title: VLSI for Artificial Intelligence and Neural Networks

  • Editors: José G. Delgado-Frias, William R. Moore

  • DOI: https://doi.org/10.1007/978-1-4615-3752-6

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Plenum Press, New York 1991

  • Hardcover ISBN: 978-0-306-44029-8Published: 31 January 1992

  • Softcover ISBN: 978-1-4613-6671-3Published: 12 November 2012

  • eBook ISBN: 978-1-4615-3752-6Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XIII, 412

  • Topics: Computer Systems Organization and Communication Networks, Electrical Engineering, Complexity

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