Importance sampling for Ising computers using one-dimensional cellular automata

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 configu...

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Vydané v:IEEE transactions on computers Ročník 38; číslo 6; s. 769 - 774
Hlavní autori: Hortensius, P.D., Card, H.C., McLeod, R.D., Pries, W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.06.1989
Institute of Electrical and Electronics Engineers
Predmet:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computational modeling
Computer architecture
Computer science; control theory; systems
Concurrent computing
Exact sciences and technology
Monte Carlo methods
Pervasive computing
Physics computing
Signal processing algorithms
Systolic arrays
Temperature
Theoretical computing
Very large scale integration
Monte Carlo method
Parallel algorithm
Cellular automaton
VLSI circuit
Computer architecture
Algorithmics
Pseudorandom number
Parallelism
Random number generation
ISSN:0018-9340
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Shrnutí: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.< >
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ISSN:0018-9340
DOI:10.1109/12.24285