Importance Sampling for Ising Computers Using One-Dimensional Cellular Automata

Abstract

This paper demonstrates that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially-distributed set of pseudorandom numbers which are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken in this paper to random number generation may also be applied to a variety of other highly nondeterministic algorithms from many fields such as computational geometry, pattern recognition, and artificial intelligence. © 1989 IEEE