Perspectives for Monte Carlo simulations on the CNN universal machine

M. Ercsey-Ravasz, T. Roska, Z. Néda

Research output: Contribution to journalArticle

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)909-922
Number of pages14
JournalInternational Journal of Modern Physics C
Issue number6
Publication statusPublished - Jun 2006


  • Cellular Neural Networks
  • Monte Carlo simulations
  • Random number generator

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Computer Science Applications
  • Computational Theory and Mathematics

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