skip to main content
10.1145/1465482.1465505acmotherconferencesArticle/Chapter ViewAbstractPublication PagesafipsConference Proceedingsconference-collections
research-article
Free Access

Stochastic computing

Authors Info & Claims
Published:18 April 1967Publication History

ABSTRACT

The Stochastic Computer was developed as part of a program of research on the structure, realization and application of advanced automatic controllers in the form of Learning Machines. Although algorithms for search, identification, policy-formation and the integration of these activities, could be established and tested by simulation on conventional digital computers, there was no hardware available which would make construction of the complex computing structure required in a Learning Machine feasible. The main problem was to design an active storage element in which the stored value was stable over long periods, could be varied by small increments, and whose output could act as a 'weight' multiplying other variables. Since large numbers of these elements would be required in any practical system it was also necessary that they be small and of low cost. Conventional analog integrators and multipliers do not fulfill requirements of stability and low cost, and unconventional elements such as electro-chemical stores and transfluxors are unreliable or require sophisticated external circuitry to make them usable. Semiconductor integrated circuits have advantages in speed, stability, size and cost, and it was decided to design a computing element based on standard gates and flip-flops which would be amenable to large-scale integration.

References

  1. J. H. Andreae Learning machines in Encyclopaedia of Information, Linguistics and Control (Pergamon Press, to be published)Google ScholarGoogle Scholar
  2. B. R. Gaines and J. H. Andreae A learning machine in the context of the general control problem Proceedings of the 3rd Congress of IFAC 1966Google ScholarGoogle Scholar
  3. G. Nagy A survey of analog memory devices IEEE Trans. Electron. Comp. 12 388 1963Google ScholarGoogle ScholarCross RefCross Ref
  4. B. R. Gaines Stochastic computers in Encyclopaedia of Information, Linguistics and Control Pergamon Press, to be publishedGoogle ScholarGoogle Scholar
  5. B. R. Gaines Techniques of identification with the stochastic computer Proc. IFAC Symp. Problems of Identification 1967Google ScholarGoogle Scholar
  6. F. V. Mayorov and Y. Chu Digital differential analysers Iliffe Books, London 1964Google ScholarGoogle Scholar
  7. H. Schmid "An operational hybrid computing system IEEE Trans. Electron Comp. 12 715 1963Google ScholarGoogle ScholarCross RefCross Ref
  8. B. R. Gaines and P. L. Joyce Phase computers 5th AICA Congress 1967Google ScholarGoogle Scholar
  9. W. W. Peterson Error correcting codes MIT Press & Wiley, New York 1961Google ScholarGoogle Scholar
  10. C. L. Becker and J. V. Wait Two-level correlation on an analog computer IRE Trans. Electron Comp. 10 752 1961Google ScholarGoogle ScholarCross RefCross Ref
  11. B. P. Th. Veltman and A. van den Bos The applicability of the relay correlator and the polarity coincidence correlator in automatic control Proc. 2nd Congress IFAC 1963Google ScholarGoogle Scholar
  12. P. Eykhoff, P. M. van der Grinten, H. Kwakernaak, B. P. Th. Veltman Systems modelling and identification Survey Paper 3rd Congress of IFAC 1966Google ScholarGoogle Scholar
  13. O. I. Elgerd High frequency signal injection: a means of changing the transfer characteristics of nonlinear elements WESCON 1962Google ScholarGoogle Scholar
  14. A. A. Pervozanskii Random processes in nonlinear control Academic Press, New York 1965Google ScholarGoogle Scholar
  15. P. Jespers, P. T. Chu, and A. Fettweis A new method to compute correlation functions Proc. Int. Symp. Inf. Theo. 1962Google ScholarGoogle Scholar
  16. G. A. Korn Random-process simulation and measurement McGraw Hill, Inc. 1966Google ScholarGoogle Scholar
  17. M. O. Rabin Probabilistic automata Inf. & Contr. 6 230 1963Google ScholarGoogle Scholar
  18. A. PAZ Some aspects of probabilistic automata Inf. & Contr. 9 26 1966Google ScholarGoogle ScholarCross RefCross Ref
  19. F. Rosenblatt A model for experimental storage in neural networks in Computer and Information Sciences Spartan Books, Washington, D.C. 1964Google ScholarGoogle Scholar
  20. B. Widrow and F. W. Smith Pattern-recognizing control systems in Computer and Information Sciences Spartan Books, Washington, D. C. 1964Google ScholarGoogle Scholar
  21. A. Novikoff Convergence proofs for perceptrons in Mathematical Theory of Automata Polytechnic Press, Brooklyn & Wiley Interscience 1963Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    AFIPS '67 (Spring): Proceedings of the April 18-20, 1967, spring joint computer conference
    April 1967
    809 pages
    ISBN:9781450378956
    DOI:10.1145/1465482

    Copyright © 1967 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 18 April 1967

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader