PERSPECTIVES FOR MONTE CARLO SIMULATIONS ON THE CNN UNIVERSAL MACHINE

M. Ercsey-Ravasz (), T. Roska () and Z. Néda ()
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M. Ercsey-Ravasz: Pázmány Péter Catholic University, Department of Information Technology, HU-1083 Budapest, Hungary;
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

International Journal of Modern Physics C (IJMPC), 2006, vol. 17, issue 06, 909-922

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.

Keywords: Monte Carlo simulations; Cellular Neural Networks; random number generator; 89.20.Ff; 05.10.Ln; 07.05.Tp (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1142/S0129183106009230

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