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Stochastic Computing Systems

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Advances in Information Systems Science

Part of the book series: Advances in Information Systems Science ((AISS))

Abstract

The invention of the steam engine in the late eighteenth century made it possible to replace the muscle-power of men and animals by the motive power of machines. The invention of the stored-program digital computer during the second world war made it possible to replace the lower-level mental processes of man, such as arithmetic computation and information storage, by electronic data-processing in machines. We are now coming to the stage where it is reasonable to contemplate replacing some of the higher mental processes of man, such as the ability to recognize patterns and to learn, with similar capabilities in machines. However, we lack the “steam engine” or “digital computer” which will provide the necessary technology for learning and pattern recognition by machines.

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References

  1. W. J. Poppelbaum and C. Afuso, Noise Computer, University of Illinois, Dept. Computer Science, Quarterly Technical Progress Reports (April 1965-January 1966).

    Google Scholar 

  2. W. J. Poppelbaum, C. Afuso, and J. W. Esch, Stochastic Computing Elements and Systems, in “Proc. American Federation of Information Processing Societies, Fall Joint Computer Conference,” Vol. 31, pp. 635–644, Books, Inc., New York (1967).

    Google Scholar 

  3. S. T. Ribeiro, Random Pulse Machines, IEEE Trans. Electronic Computers EC-16 261–276 (June 1967).

    Google Scholar 

  4. B. R. Gaines, Stochastic Computing, in “Proc. American Federation of Information Processing Societies, Spring Joint Computer Conference,” Vol. 30, pp. 149–156, Books, Inc., New York (1967).

    Google Scholar 

  5. B. R. Gaines, Techniques of Identification with the Stochastic Computer, in “Proc. International Federation of Automatic Control Symposium on Identification, Progue, June 1967.”

    Google Scholar 

  6. B. R. Gaines, Stochastic Computer Thrives on Noise, Electronics 40(14), 72–79 (July 10 1967).

    Google Scholar 

  7. B. R. Gaines, Stochastic Computing, in “Encyclopaedia of Information, Linguistics and Control,” pp. 766–781, Pergamon Press, New York and London (1968).

    Google Scholar 

  8. J. T. Tou, Engineering Principles of Pattern Recognition, in “Advances in Information Systems Science,” Vol. 1, J. T. Tou, ed., Plenum Press, New York (1969).

    Chapter  Google Scholar 

  9. K. S. Fu, Learning Control Systems, in “Advances in Information Systems Science,” Vol. 1, J. T. Tou, ed., Plenum Press, New York (1969).

    Google Scholar 

  10. J. H. Andreae, Learning Machines—A Unified View, in “Encyclopaedia of Information, Linguistics and Control,” Pergamon Press, New York and London (1968).

    Google Scholar 

  11. E. Feigenbaum and J. Feldman, “Computers and Thought,” McGraw-Hill Book Co., New York (1964).

    Google Scholar 

  12. L. Uhr, “Pattern Recognition,” John Wiley and Sons, New York (1966).

    Google Scholar 

  13. J. T. Tou and R. H. Wilcox, “Computer and Information Sciences,” Spartan Books, Washington, D.C. (1964).

    Google Scholar 

  14. W. J. Karplus and J. A. Howard, A Transfluxor Analog Memory Using Frequency Modulation, in “American Federation of Information Processing Societies,” Vol. 26, Part 1, pp. 673–683, Books, Inc., New York.

    Google Scholar 

  15. W. J. Karplus, A Hybrid Computer Technique for Treating Nonlinear Partial Differential Equations, IEEE Trans. Electronic Computers EC-13 (5) 597–605 (1964).

    Google Scholar 

  16. B. R. Gaines and J. H. Andreae, A Learning Machine in the Context of the General Control Problem, in “Proc. 3rd International Congress International Federation of Automatic Control, London, 1966,” Institution of Mechanical Engineers, London (1967).

    Google Scholar 

  17. W. J. Poppelbaum, Hybrid Graphical Processors, in “Computer Technology,” IEEE Conf. Publ., Vol. 32 (July 1967).

    Google Scholar 

  18. T. J. Williams, Process Dynamics, in “Proc. 2nd International Congress International Federation of Automatic Control, Basle, 1963.”

    Google Scholar 

  19. G. A. Korn and T. M. Korn, “Electronic Analog and Hybrid Computers,” McGraw-Hill Book Co., New York (1964).

    Google Scholar 

  20. J. R. Smith and C. O. Harbourt, An Adaptive Threshold Logic Gate Using Capacitive Analog Weights, IEEE Trans. Electronic Computers EC-17 (1), 78–81 (1968).

    Article  Google Scholar 

  21. H. Schmid, Sequential Analog-Digital Computer (SADC), in “American Federation of Information Processing Societies, Joint Computer Conference,” Vol. 27, Part 1, pp. 915–928, Books, Inc., New York (1965).

    Google Scholar 

  22. D. L. Greer, Characterization of the Magnetic Second-Harmonic Memory, IEEE Trans. Electronic Computers EC-17 (6), 551–558 (1968).

    Article  Google Scholar 

  23. B. Widrow, An Adaptive ADALINE Neuron Using Chemical Memistors, ERL Tech. Rep. No. 1553–2, Stanford University (1960).

    Google Scholar 

  24. G. Nagy, A Survey of Analog Memory Devices, IEEE Trans. Electronic Computers EC-12 388–393 (August 1963).

    Article  Google Scholar 

  25. S. Larach, “Photoelectronic Materials and Devices,” D. van Nostrand, Englewood Cliffs, New Jersey (1965).

    Google Scholar 

  26. D. R. Bosomworth and H. J. Gerritsen, Thick Holograms in Photocromic Materials, Appl. Optics 7(1), 95–98(1968).

    Article  Google Scholar 

  27. G. U. Kaiman, Holographic Graphical Storage in Thick Alkali-Halide Crystals, IEEE Int. Conv. Digest, p. 35(1968).

    Google Scholar 

  28. J. K. Hawkins and C. J. Munsey, Parallel Computer Organizations and Mechanizations, IEEE Trans. Electronic Computers EC-12 (3), 251–262 (1963).

    Article  Google Scholar 

  29. D. K. Pollock, C. J. Koester, and J. T. Tippett, “Optical Processing of Information,” Spartan Books, Washington, D.C. (1963).

    Google Scholar 

  30. S. J. Mathis, R. E. Wiley, and L. M. Spandorfer, “Microelectronics and Large Systems,” Spartan Books, Washington, D.C. (1965).

    Google Scholar 

  31. R. L. Petritz, Technological Foundations and Future Directions of Large-Scale Integrated Electronics, in “American Federation of Information Processing Societies, Fall Joint Computer Conference,” Vol. 29, pp. 65–87, Books, Inc., New York (1966).

    Google Scholar 

  32. D. L. Slotnick, W. C. Borck, and R. C. McReynolds, The Solomon Computer— A Preliminary Report, in “Computer Organization,” pp. 66–92, Spartan Books, Washington, DC, (1963).

    Google Scholar 

  33. W. A. Clark, S. M. Orstein, M. J. Stuki, A. S. Blum, T. J. Chaney, R. E. Olsen, R. A. Dammhoehler, W. E. Ball, C. E. Moinar, A. Antharvedi, Macromodular Computer Systems, in “American Federation of Information Processing Societies, Spring Joint Computer Conference,” Vol. 30, pp. 355–401, Books, Inc., New York (1967).

    Google Scholar 

  34. R. H. Fuller and R. M. Bird, An Associative Parallel Processor with Application to Picture Processing, in “American Federation of Information Processing Societies, Fall Joint Computer Conference,” Vol. 27, Part 1, pp. 105–116, Books, Inc., New York (1965).

    Google Scholar 

  35. J. C. Murtha, Highly Parallel Information Processing Systems, in “Advances in Computers,” Vol. 7, pp. 1–116 Academic Press, New York (1966).

    Google Scholar 

  36. E. L. Braun, “Digital Computer Design,” Chapter 8, Academic Press, New York (1963).

    Google Scholar 

  37. F. V. Mayorov and Y. Chu, “Digital Differential Analysers,” Iliffe Books (1964).

    Google Scholar 

  38. W. J. Karplus, Analog and Digital Techniques Combined, in “Computer Control Systems Technology,” pp. 148–155 McGraw-Hill Book Co., New York (1961).

    Google Scholar 

  39. Digital Operational Techniques, Computer Design 1963 (November), 12.

    Google Scholar 

  40. B. R. Gaines and P. L. Joyce, Phase Computers, in “Proc. 5th International Congress AICA, Lausanne, 1967.”

    Google Scholar 

  41. B. R. Gaines, A Modular Programmed DDA for Real-Time Computation, in “Proc. IFIP 68, Edinburgh, 1968.”

    Google Scholar 

  42. W. R. Schumann, Method and Apparatus for Averaging a Series of Transients, United States Patent 3, 182, 181 (May 5, 1965).

    Google Scholar 

  43. B. P. Th. Veltman and A. van den Bos, The Applicability of the Relay Correlator and Polarity Coincidence Correlator in Automatic Control, in “Proc. 2nd International Congress International Federation of Automatic Control,” Basle, 1963.

    Google Scholar 

  44. W. W. Peterson, “Error Correcting Codes,” John Wiley and Sons, New York (1961).

    Google Scholar 

  45. W. H. Kautz, “Linear Sequential Switching Circuits,” Holden-Day, San Francisco (1965).

    Google Scholar 

  46. S. W. Golomb, “Shift Register Sequences,” Holden-Day, San Francisco (1967).

    Google Scholar 

  47. A. Gill, “Finite-State Machines,” McGraw-Hill Book Co., New York (1962).

    Google Scholar 

  48. A. Gill, “Linear Sequential Circuits,” McGraw-Hill Book Co., New York (1967).

    Google Scholar 

  49. G. A. Korn, “Random-Process Simulation and Measurements,” McGraw-Hill Book Co., New York (1966).

    Google Scholar 

  50. P. A. N. Briggs, P. H. Hammond, M. T. G. Hughes, and G. O. Plumb, Correlation Analysis of Process Dynamics Using Pseudo-Random Binary Test Perturbations, in “Advances in Automatic Control,” pp. 37–51, Institution of Mechanical Engineers, London (1965).

    Google Scholar 

  51. R. P. Gilson, Some Results of Amplitude Distribution Experiments on Shift-Register Generated Pseudo-Random Noise, IEEE Trans. Electronic Computers EC-15 (6), 926–927 (1966).

    Google Scholar 

  52. R. C. White, Experiments with Digital Computer Simulations of Pseudo-Random Noise Generators, IEEE Trans. Electronic Computers EC-16 (3), 355–357 (1967).

    Article  Google Scholar 

  53. R. E. Kaiman, Nonlinear Aspects of Sampled-Data Control Systems, in “Proc. Symp. Nonlinear Circuit Analysis,” pp. 273–313, Polytechnic Institute of Brooklyn, New York (1957).

    Google Scholar 

  54. M. Arbib, Tolerance Automata, in “Kybernetika Cislo 3,” pp. 223–233 (1967).

    Google Scholar 

  55. W. De Backer and L. Verbeek, Study of Analog, Digital, and Hybrid Computers Using Automata Theory, ICC Bulletin 5, 215–244 (1966).

    Google Scholar 

  56. M. Arbib, “Algebraic Theory of Machines, Languages and Semigroups,” Academic Press, New York (1968).

    Google Scholar 

  57. K. H. Hofman and P. S. Mostert, “Elements of Compact Semigroups,” Merrill Books, Columbus, Ohio (1966).

    Google Scholar 

  58. J. T. Tou, ed., “Applied Automata Theory,” Academic Press, New York (1968).

    Google Scholar 

  59. M. O. Rabin, Probabilistic Automata, Information and Control 6, 230–245 (1963).

    Article  Google Scholar 

  60. A. Paz, Some Aspects of Probabilistic Automata, Information and Control 9, 26–60 (1966).

    Article  Google Scholar 

  61. W. Feller, “An Introduction to Probability Theory and Its Applications,” John Wiley and Sons, New York (1957).

    Google Scholar 

  62. L. Takacs, “Stochastic Processes,” Methuen, London (1960).

    Google Scholar 

  63. R. McNaughton, The Theory of Automata—A Survey, in “Advances in Computers,” Vol. 2, pp. 379–421, Academic Press, New York (1961).

    Google Scholar 

  64. J. Fox, “Mathematical Theory of Automata,” Polytechnic Institute of Brooklyn Press, New York (1963).

    Google Scholar 

  65. T. L. Booth, “Sequential Machines and Automata Theory and Sons, New York” John Wiley (1967).

    Google Scholar 

  66. G. C. Bacon, Minimal-State Stochastic Finite State Systems, IEEE Trans. CT-11, 307–308 (1964).

    Google Scholar 

  67. G. H. Ott, Reconsider the Minimization Problem for Stochastic Finite State Systems, in “Proc. IEEE 7th Symp. Switching and Automata Theory,” pp. 251–261 (1966).

    Google Scholar 

  68. G. C. Bacon, The Decomposition of Stochastic Automata, Information and Control 7, 320–339 (1964).

    Article  Google Scholar 

  69. C. V. Page, Equivalences between Probabilistic Machines, Tech. Rep. 03105–41-T, University of Michigan (1965).

    Google Scholar 

  70. J. W. Carlyle, State-Calculable Stochastic Sequential Machines, Equivalences and Events, in “Proc. IEEE Symp. Switching Circuit Theory and Logical Design,” pp. 258–263 (1965).

    Google Scholar 

  71. J. von Neumann, Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components, in “Automata Studies,” Princeton University Press, Princeton, New Jersey (1956).

    Google Scholar 

  72. S. Winograd and J. D. Cowan, “Reliable Computation in the Presence of Noise,” MIT Press, Cambridge, Mass. (1963).

    Google Scholar 

  73. J. D. Cowan, The Problem of Organismic Reliability, in “Cybernetics of the Nervous System,” Elsevier Amsterdam (1965).

    Google Scholar 

  74. F. Rosenblatt, A Model for Experiental Storage in Neural Networks, in “Computer and Information Sciences,” (J. T. Tou and R. H. Wilcox, eds.), pp. 16–66, Spartan Books Washington, D.C. (1964).

    Google Scholar 

  75. B. Widrow and F. W. Smith, Pattern-Recognizing Control Systems, in “Computer and Information Sciences,” (J. T. Tou and R. H. Wilcox, eds.), pp. 288–317, Spartan Books Washington, D.C. (1964).

    Google Scholar 

  76. K. Steinbuch and U. A. W. Piske, Learning Matrices and Their Applications, IEEE Trans. Electronic Computers EC-12 (5), 846–862 (1963).

    Article  Google Scholar 

  77. N. J. Nilsson, “Learning Machines,” McGraw-Hill Book Co., New York (1965).

    Google Scholar 

  78. A. Novikoff, On Convergence Proofs for Perceptrons, in “Automata Theory,” pp. 615–622, Polytechnic Institute of Brooklyn Press, New York (1963).

    Google Scholar 

  79. G. L. Clapper, Machine Looks, Listens, Learns, Electronics 1967 (October 30), 91–102.

    Google Scholar 

  80. W. C. Ridgeway, An Adaptive Logic System with Generalizing Properties, Report SEL-62–040, Stanford Electronics Laboratories (April 1962).

    Google Scholar 

  81. I. Aleksander and R. C. Albrow, Adaptive Logic Circuits, Computer J. 11 (1), 65–71 (1968).

    Article  Google Scholar 

  82. S. Muroga, Lower Bounds on the Number of Threshold Functions and a Maximum Weight, IEEE Trans. Electronic Computers EC-14 136–148 (1965).

    Article  Google Scholar 

  83. Sze-Tsen Hu, “Threshold Logic,” University of California Press, Berkeley, Calif. (1965).

    Google Scholar 

  84. M. Cuenod and A. P. Sage, Comparison of Some Methods Used for Process Identification, in “Proc. International Federation of Automatic Control Symposium on Identification in Automatic Control Systems, Prague, June 1967.”

    Google Scholar 

  85. P. Eykhoff, Process Parameter and State Estimation, in “Proc. International Federation of Automatic Control Symposium on Identification in Automatic Control Systems, Prague, June 1967.”

    Google Scholar 

  86. M. J. Levin, Optimum Estimation of Impulse Response in the Presence of Noise, IRE Natl. Com. Record 4, 147–182 (1959).

    Google Scholar 

  87. W. W. Lichtenberger, A Technique of Linear System Identification Using Correlating Filters, IRE Trans. Automatic Control AC-6 (2), 183–199 (1961).

    Article  Google Scholar 

  88. Y. Kaya and S. Yamamura, A Self-Adaptive System with a Variable Parameter PID Control, AIEE Trans. Appln. Ind. 58, 378–386 (1962).

    Google Scholar 

  89. M. Margolis and C. T. Leondes, A Model-Referenced Parameter Tracking Technique for Adaptive Control Systems, IEEE Trans. Appln. Ind. 68, 241–261 (1963).

    Google Scholar 

  90. B. G. Madden, Simultaneous Determination of System Parameters from Transient Response, IEEE Trans. Appln. Ind. 69, 327–331 (1963).

    Article  Google Scholar 

  91. P. C. Young, The Determination of the Parameters of a Dynamic Process, Radio and Electronic Engineer 29 (6), 345–361 (1965).

    Article  Google Scholar 

  92. D. D. Donalson and F. H. Kishi, Review of Adaptive Control System Theories and Techniques, in “Modern Control Systems Theory,” McGraw-Hill Book Co., New York (1965).

    Google Scholar 

  93. P. E. K. Donaldson, Error Decorrelation Studies on a Human Operator Performing a Balancing Task, Med. Electron. Biol. Eng. 2, 393–410 (1964).

    Article  Google Scholar 

  94. T. F. Potts, G. N. Ornstein, and A. B. Clymer, The Automatic Determination of Human and Other System Parameters, in “Proc. Western Joint Computer Conference, Los Angeles, pp. 645–660 (1961).

    Google Scholar 

  95. C. L. Becker and J. V. Wait, Two-Level Correlation on an Analog Computer, IRE Trans. Electronic Computers EC-10 (4), 752–758 (1961).

    Article  Google Scholar 

  96. Y. Lundh, A Digital Integrator for On-line Signal Processing, IEEE Trans. Electronic Computers EC-12 (1), 26–28 (1963).

    Article  Google Scholar 

  97. P. Jespers, P. T. Chu, and A. Fettweis, A New Method to Compute Correlation Functions, in “International Symp. Information Theory, Brussels, 1962.”

    Google Scholar 

  98. B. Widrow, Statistical Analysis of Amplitude-Quantized Sampled Data Systems, IRE Trans. CT-3 (1956).

    Google Scholar 

  99. D. G. Watts, “A General Theory of Amplitude Quantization with Applications to Correlation Determination,” IEE Monograph No. 481 M (November 1961).

    Google Scholar 

  100. A. K. Nath and A. K. Mathalanabis, Method of Statistical Linearization, Proc. IEE 113 (12), 2081–2086 (1966).

    Google Scholar 

  101. A. A. Pervozanskii, “Random Processes in Nonlinear Control,” Academic Press, New York (1965).

    Google Scholar 

  102. O. I. Elgard, High-Frequency Signal Injection : A Means of Changing the Transfer Characteristics of Nonlinear Elements, WESCON (1962).

    Google Scholar 

  103. G. R. Cooper, R. L. Gassner, and C. D. McGillem, in “Proc. 21st National Electronics Conference,” pp. 656–661, National Electronics Conference, Chicago, Illinois (1965).

    Google Scholar 

  104. L. J. Savage, “The Foundations of Statistical Inference,” Methuen, London (1962).

    Google Scholar 

  105. S. Watanabe, Information-Theoretical Aspects of Inductive and Deductive Inference, IBM J. Res. Dev. 14 (2), 208–231 (1960).

    Article  Google Scholar 

  106. D. Middleton, “Topics in Communication Theory,” McGraw-Hill Book Co., New York (1965).

    Google Scholar 

  107. M. E. Maron, Design Principles for an Intelligent Machine, IRE Trans. Information Theory IT-8 (5), 179–185 (1962).

    Article  Google Scholar 

  108. M. Minsky and O. G. Selfridge, Learning in Random Nets, in “Information Theory,” pp. 335–347, Butterworths, London (1961).

    Google Scholar 

  109. K. S. Fu, A Learning Control System Using Statistical Decision Processes, in “Proc. International Federation of Automatic Control Symp. Self-Adaptive Control Systems, Teddington, England, 1965,” Plenum Press, New York (1965).

    Google Scholar 

  110. D. R. Hill, An ESOTerIC Approach to Automatic Speech Recognition, Int. J. Man-Machine Studies 1(1) (January 1969).

    Google Scholar 

  111. S. Kullback, “Information Theory and Statistics,” John Wiley and Sons, New York (1959).

    Google Scholar 

  112. B. G. Farley and W. A. Clark, Simulation of Self-Organizing Systems by Digital Computer, IRE Trans. Information Theory IT-4, 76–84 (September 1954).

    Google Scholar 

  113. R. L. Beurle, Properties of a Mass of Cells Capable of Regenerating Pulses, Trans. Roy. Soc. (London) B240, 55–94 (August 1956).

    Article  Google Scholar 

  114. L. D. Harmon, W. A. Bergeijk, and J. Levinson, Studies with Artificial Neurons, Kybernetik 1, 89–117 (December 1961).

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering Science, University of Essex, Colchester, Essex, UK

    B. R. Gaines

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  1. College of Engineering, University of Florida, Gainesville, Florida, USA

    Julius T. Tou

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Gaines, B.R. (1969). Stochastic Computing Systems. In: Tou, J.T. (eds) Advances in Information Systems Science. Advances in Information Systems Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-5841-9_2

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