Perspectives for Monte Carlo simulations on the CNN Universal Machine

Details Perspectives for Monte Carlo simulations on the CNN Universal Machine Perspectives for Monte Carlo simulations on the CNN Universal Machine

2006-03-15

arxiv.org/abs/physics/0603121v1

Physics

Computational Physics

14 pages, 6 figures

Scientific paper

10.1142/S0129183106009230

Possibilities for performing stochastic simulations on the analog and fully parallelized Cellular Neural Network Universal Machine (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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