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.Comment: 14 pages, 6 figure