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Perspectives For Monte Carlo Simulations On The Cnn Universal Machine

Author

Listed:
  • M. ERCSEY-RAVASZ

    (Pázmány Péter Catholic University, Department of Information Technology, HU-1083 Budapest, Hungary;
    Babeş-Bolyai University, Department of Physics, RO-400084 Cluj, Romania)

  • T. ROSKA

    (Pázmány Péter Catholic University, Department of Information Technology, HU-1083 Budapest, Hungary)

  • Z. NÉDA

    (Babeş-Bolyai University, Department of Physics, RO-400084 Cluj, Romania)

Abstract

Possibilities for performing stochastic simulations on the analog and fully parallelized Cellular Neural Network UniversalMachine (CNN-UM) are investigated. By using a chaotic cellular automaton perturbed with the natural noise of the CNN-UM chip, a realistic binary random number generator is built. As a specific example for Monte Carlo type simulations, we use this random number generator and a CNN template to study the classical site-percolation problem on the ACE16K chip. The study reveals that the analog and parallel architecture of the CNN-UM is very appropriate for stochastic simulations on lattice models. The natural trend for increasing the number of cells and local memories on the CNN-UM chip will definitely favor in the near future the CNN-UM architecture for such problems.

Suggested Citation

  • M. Ercsey-Ravasz & T. Roska & Z. Néda, 2006. "Perspectives For Monte Carlo Simulations On The Cnn Universal Machine," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 909-922.
    • Handle: RePEc:wsi:ijmpcx:v:17:y:2006:i:06:n:s0129183106009230
    DOI: 10.1142/S0129183106009230
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