Journal
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
Volume 17, Issue 6, Pages 909-922Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129183106009230
Keywords
Monte Carlo simulations; Cellular Neural Networks; random number generator
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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.
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