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Possibilities for performing stochastic simulations on the analog and fullyparallelized Cellular Neural Network Universal Machine (CNN-UM) areinvestigated. By using a chaotic cellular automaton perturbed with the naturalnoise 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 randomnumber generator and a CNN template to study the classical site-percolationproblem on the ACE16K chip. The study reveals that the analog and parallelarchitecture of the CNN-UM is very appropriate for stochastic simulations onlattice models. The natural trend for increasing the number of cells and localmemories on the CNN-UM chip will definitely favor in the near future the CNN-UMarchitecture for such problems.Comment: 14 pages, 6 figure

PERSPECTIVES FOR MONTE CARLO SIMULATIONS ON THE CNN UNIVERSAL MACHINE