VLSI for Artificial Intelligence and Neural Networks

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

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.

• Hardware
• Symbol
• VLSI
• algorithm
• analog
• architecture
• artificial intelligence
• complexity
• computer
• computer architecture
• logic
• network
• neural network
• neural networks
• programming

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

  José G. Delgado-Frias

• Oxford University, Oxford, UK

  William R. Moore

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