Statistical mechanics and disordered systems
Communications of the ACM - Lecture notes in computer science Vol. 174
Random sequence generation by cellular automata
Advances in Applied Mathematics
VLSI Signal Processing; A Bit-Serial Approach
VLSI Signal Processing; A Bit-Serial Approach
VLSI and Modern Signal Processing
VLSI and Modern Signal Processing
Parallel computation of non-deterministic algorithms in vlsi
Parallel computation of non-deterministic algorithms in vlsi
Cellular Automata
Parallel Random Number Generation for VLSI Systems Using Cellular Automata
IEEE Transactions on Computers
Cellular Automata-Based Recursive Pseudoexhaustive Test Pattern Generator
IEEE Transactions on Computers
FPGA implementation of neighborhood-of-four cellular automata random number generators
FPGA '02 Proceedings of the 2002 ACM/SIGDA tenth international symposium on Field-programmable gate arrays
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The authors demonstrate 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 that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence.