Perspectives for Monte Carlo simulations on the CNN Universal Machine
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.
Cite
@article{arxiv.physics/0603121,
title = {Perspectives for Monte Carlo simulations on the CNN Universal Machine},
author = {M. Ercsey-Ravasz and T. Roska and Z. Neda},
journal= {arXiv preprint arXiv:physics/0603121},
year = {2009}
}
Comments
14 pages, 6 figures