Implementing Neural Networks with the Associative String Processor

  • Anargyros Krikelis
  • Michael Grözinger


The rebirth of activity in the area of neural computation is stimulated by the increasing frequency with which traditional computational paradigms appear to inefficiently handle fuzzy problems of large dimensionality (e.g. pattern recognition, associative information retrieval, etc.) and the technological advances. Indeed, with the huge strides in VLSI and WSI technologies and the emergence of electro-optics, massively parallel systems that were unrealisable only a few years ago are coming within reach.

The paper details the efficient implementation of two neural network models (i.e. Hopfield’s relaxation model and the back propagation model) on a massively parallel, programmable, fault-tolerant architecture, the ASP (Associative String Processor), which can efficiently support low-MIMD/high-SIMD and other parallel computation paradigms.

Indeed, the paper describes the mapping of the two neural networks, details the steps required to execute the network computations and reports the performance of the ASP implementations which achieved computational rate of Giga-interconnections/sec (i.e. 10 9 interconnections per sec).


Hide Node Synaptic Weight Virtual Node Hopfield Model Parallel Computer Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Lea, R. M., “The ASP: a cost-effective parallel microcomputer” IEEE Micro, pp. 10–29, October 1988,.Google Scholar
  2. Hopfield, J. J. “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proceedings of the National Academy of Science, USA, Vol. 79, pp. 2554–2558, April 1982.MathSciNetCrossRefGoogle Scholar
  3. Jones, W. P. and Hoskins, J., “Back-Propagation”, BYTE, pp. 155–162, October 1987.Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Anargyros Krikelis
    • 1
  • Michael Grözinger
    • 2
  1. 1.Aspex Microsystems Ltd.Brunel UniversityUxbridgeUK
  2. 2.Department of Electrical Engineering and ElectronicsBrunel UniversityUxbridgeUK

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