Abstract
The cellular neural network (CNN) architecture combines the best features from traditional fully-connected analog neural networks and digital cellular automata. The network can rapidly process continuous-valued (gray-scale) input signals (such as images) and perform many computation functions which traditionally were implemented in digital form. Here, we briefly introduce the the theory of CNN circuits, provide some examples of CNN applications to image processing, and discuss work toward a CNN implementation in custom CMOS VLSI. The role of analog computer-aided design (CAD) will be briefly presented as it relates to analog neural network implementation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J.J. Hopfield, “Neural networks and physical systems with emergent computational abilities”,Proc. Natl. Acad. Sci. USA, vol. 79, 1982, pp. 2554–2558.
J.J. Hopfield and D. W. Tank, “Computing with neural circuits: a model,”Science (USA), vol. 233, no. 4764, 1986, pp. 625–633.
D.E. Rumelhart and J.L. McClelland,Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, Cambridge, MA: M.I.T. Press, 1986.
K. Preston, Jr., and M. J. B. Duff,Modern Cellular Automata: Theory and Applications, New York: Plenum Press, 1984.
T. Toffoli and N. Margolus,Cellular Automata Machines—a new environment for modeling, Cambridge, MA: M.I.T. Press, 1986.
J. von Neumann,The Computer and the Brain, New Haven: Yale University Press, 1958.
J. von Neumann, “The general logical theory of automata,”Cerebral Mechanisms in Behavior—The Hixon Symposium, New York: Wiley, 1951.
S. Wolfram,Theory and applications of cellular automata, World Scientific Publishing Co., 1986.
L.O. Chua and L. Yang, “Cellular Neural Networks: Theory,”IEEE Trans. Circuits and Systems, vol. 35, no. 10, 1988, pp. 1257–1272.
L.O. Chua and L. Yang, “Cellular Neural Networks: Applications,”IEEE Trans. Circuits and Systems, vol. 35, no. 10, 1988, pp. 1273–1290.
L.O. Chua, C.A. Desoer, and E.S. Kuh,Linear and Nonlinear Circuits, New York: McGraw-Hill, 1987.
L.O. Chua,Introduction to Nonlinear Network Theory, New York: McGraw-Hill, 1970, (987 pages)
L.O. Chua and P.M. Lin,Computer-aided Analysis of Electronic Circuits: Algorithms and Computational Techniques, Englewood Cliffs, NJ: Prentice Hall 1975, p. 72.
L.O. Chua and R. Ying, “Finding All Solutions of Piecewise-Linear Circuits,”International Journal of Circuit Theory and Applications, vol. 10, 1982, pp. 201–229.
J. Hopfield and D. Tank, “Neural' Computation of Decisions in Optimization Networks,”Biological Cybernetics, vol. 52, 1985, pp. 141–152.
J. Hopfield and D. Tank, “Collective Computation with Continous Variables,” inDisordered Systems and Biological Organization, (ed. E. Bienenstock, F. Fogelman & G. Weisbuch), Berlin: Springer-Verlag, 1985.
R.L. McEleice, E.C. Posner, E.R. Rodemich, and S.S. Venkatesh, “The Capacity of the Hopfield Associative Memory,”IEEE Trans. on Information Theory vol. IT-33, no. 4, 1987, pp. 461–482.
L.O. Chua and T. Roska, “Stability of a Class of Nonreciprocal Cellular Neural Networks,”IEEE Trans. on Circuits and Systems, vol. 37, 1990, pp. 1520–1527.
W.S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,”Bulletin of Mathematical Bioophysics, vol. 5, 1943, pp. 115–133.
W. Pitts and W. McCulloch, “How we know universals: the perception of auditory and visual forms,”Bulletin of Mathematical Biophysics, vol. 9, 1947, pp. 127–147.
H.D. Block, “The Perceptron: a model for brain functioning. I,”Reviews of Modern Physics, vol. 34, 1962, pp. 123–135.
M. Minsky and S. Papert,Perceptrons: An Introduction of Computational Geometry, Cambridge, MA: M.I.T. Press, 1969.
T. Matsumoto, L.O. Chua, and H. Suzuki, “CNN Cloning Template: Connected Component Detector,IEEE Trans. on Circuit and Systems, vol. 37, 1990, pp. 633–635.
M.P. Kennedy and L.O. Chua, “Circuit Theoretic Solutions for Neural Networks—an old approach to a new problem,”Proc. First ICNN, vol. 2, San Diego, CA: IEEE, 1987, pp. 169–176.
M.J.B. Duff and T.J. Fountain,Cellular Logic Image Processing, London: Academic Press, 1986.
Anil K. Jain,Fundamentals of Digital Image Processing, Englewood Cliffs, NJ: Prentice-Hall, 1989.
L.O. Chua and B. Shi,Exploiting Cellular Automata in the Design of Cellular Neural Networks for Binary Image Processing, UCB/ERB M89/130, November 15, 1989.
T. Kohonen,Self-Organization and Associative Memory New York: Springer-Verlga, 1984.
T. Matsumoto, L.O. Chua and T. Yokohama, “Image Thinning With A cellular Neural Network,”IEEE Trans. on Circuits and Systems, vol. 37, 1990, pp. 638–640.
P.M. Morse and H. Feshback,Methods of Theoretical Physics, Part I, New York: McGraw-Hill, 1953.
M.A. Sivilotti, M.A. Mahowald, and C.A. Mead, “Real-time visual computations using analog CMOS processing arrays,”Advanced Research in VLSI: Proceedings of the 1987 Stanford Conference. Cambridge, MA: M.I.T. Press, 1987, p. 295.
T. Matsumoto, L.O. Chua, and Furukawa, “CNN Cloning Template: Hole Filler,”IEEE Trans. on Circuits and Systems, vol. 37, pp. 635–638.
Proceedings of the 1990 IEEE International Workshop on Cellular Neural Networks and Their Applications, December 16–19, 1990, Budapest, Hungary.
J.M. Cruz and L.O. Chua, “A CNN chip for connected component detection,”IEEE Trans. on Circuits and Systems, vol. 38, 1991.
Author information
Authors and Affiliations
Additional information
This work is supported in part by the Office of Naval Research under Contract N00014-89-J1402, and the National Science Foundation under grant MIP-8912639.
Rights and permissions
About this article
Cite this article
Chua, L.O., Yang, L. & Krieg, K.R. Signal processing using cellular neural networks. J VLSI Sign Process Syst Sign Image Video Technol 3, 25–51 (1991). https://doi.org/10.1007/BF00927833
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF00927833