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Perspectives for Monte Carlo simulations on the CNN Universal Machine

M. Ercsey-Ravasz·T. Roska·Z. Neda
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Abstract

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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M. Ercsey-Ravasz, T. Roska, Z. Neda. 2006-03-15. Perspectives for Monte Carlo simulations on the CNN Universal Machine. https://doi.org/10.1142/s0129183106009230

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