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On the Implementation of a Margolus Neighborhood Cellular Automata on FPGA

  • Joaquín Cerdá
  • Rafael Gadea
  • Vicente Herrero
  • Angel Sebastiá
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2778)

Abstract

Margolus neighborhood is the easiest form of designing Cellular Automata Rules with features such as invertibility or particle conserving. In this paper we propose two different implementations of systems based on this neighborhood: The first one corresponds to a classical RAM-based implementation, while the second, based on concurrent cells, is useful for smaller systems in which time is a critical parameter. This implementation has the feature that the evolution of all the cells in the design is performed in the same clock cycle.

Keywords

Cellular Automaton Clock Cycle Cellular Automaton Sequential Implementation Embed Memory 
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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References

  1. 1.
    Wolfram, S.: Cellular Automata. Los Alamos Science 9, 2–21 (1983)Google Scholar
  2. 2.
    Wolfram, S.: Statistical mechanics of cellular automata. Reviews of Modern Physics 55, 601–644 (1983)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Margolus, N.: Physics-Like models of computation. Physica 10D, 81–95 (1984)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Toffoli, T.: Cellular Automata as an alternative to (rather than an approximation of) Differential Equations in Modeling Physics. Physica 10D, 117–127 (1984)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Toffoli, T.: Occam, Turing, von Neumann, Jaynes: How much can you get for how little (A conceptual introduction to cellular automata). The Interjournal (October 1994)Google Scholar
  6. 6.
    Toffoli, T., Margolus, N.: Invertible cellular automata: a review. Physica D, Nonlinear Phenomena 45, 1–3 (1990)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gruau, F.C., Tromp, J.T.: Cellular Gravity. Parallel Processing Letters 10(4), 383–393 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Smith, M.A.: Cellular Automata Methods in Mathematical Physics. Ph.D. Thesis. MIT Department of Physics (May 1994)Google Scholar
  9. 9.
    Wolfram, S.: Cryptography with Cellular Automata. In: Williams, H.C. (ed.) CRYPTO 1985. LNCS, vol. 218, pp. 429–432. Springer, Heidelberg (1986)Google Scholar
  10. 10.
    Sarkar, P.: A brief history of cellular automata. ACM Computing Surveys 32(1), 80–107 (2000)CrossRefGoogle Scholar
  11. 11.
    Shackleford, B., Tanaka, M., Carter, R.J., Snider, G.: FPGA Implementation of Neighborhood-of-Four Cellular Automata Random Number Generators. In: Proceedings of FPGA, pp. 106–112 (2002)Google Scholar
  12. 12.
    Vichniac, G.Y.: Simulating Physics with Cellular Automata. Physica 10D, 96–116 (1984)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Popovici, A., Popovici, D.: Cellular Automata in Image Processing. In: Proceedings of MTNS 2002 (2002)Google Scholar
  14. 14.
    Wolfram, S.: A New Kind of Science. Wolfram media (2002)Google Scholar
  15. 15.
    Corno, F., Rebaudengo, M., Reorda, M.S., Squillero, G., Violante, M.: Low power BIST via hybrid cellular automata. In: 18th IEEE VLSI Test Symposium (2000)Google Scholar
  16. 16.
    Corno, F., Reorda, M.S., Squillero, G.: Exploiting the selfish gene algorithm for evolving hardware cellular automata. In: Proceedings of Congress of Evolutionary Computation (CEC 2000) (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joaquín Cerdá
    • 1
  • Rafael Gadea
    • 1
  • Vicente Herrero
    • 1
  • Angel Sebastiá
    • 1
  1. 1.Group of Digital Systems Design, Dept. Of Electronic EngineeringUniversidad Politécnica de ValenciaValenciaSpain

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