  The computational paradigm represented by Cellular Neural/nonlinear Networks
(CNN) and the CNN Universal Machine (CNN-UM) as a Cellular Wave Computer, gives
new perspectives for computational physics. Many numerical problems and
simulations can be elegantly addressed on this fully parallelized and analogic
architecture. Here we study the possibility of performing stochastic
simulations on this chip. First a realistic random number generator is
implemented on the CNN-UM, and then as an example the two-dimensional Ising
model is studied by Monte Carlo type simulations. The results obtained on an
experimental version of the CNN-UM with 128 * 128 cells are in good agreement
with the results obtained on digital computers. Computational time measurements
suggests that the developing trend of the CNN-UM chips - increasing the lattice
size and the number of local logic memories - will assure an important
advantage for the CNN-UM in the near future.
