Random Number Generator and Monte Carlo type Simulations on the CNN-UM
Mária Ercsey-Ravasz, Tamás Roska, Zoltán Néda · 2006
The computational paradigm represented by cellular neural networks (CNN) gives new perspectives also for computational physics. Here we study the possibility of performing stochastic simulations on the CNN universal machine (CNN-UM). First by using a chaotic cellular automaton perturbed with the natural noise of the CNN-UM chip, a realistic binary random number generator (RNG) is built. Using this RNG the site-percolation problem and the two-dimensional Ising model is studied by Monte Carlo type simulations. The results obtained on an ACE16K chip are in good agreement with the results obtained on digital computers. Computational time measurements suggest that the developing trend of the CNN-UM chips could assure an important advantage for the CNN-UM in the near future