dorsal/arxiv
View SchemaStochastic Simulations on the Cellular Wave Computers
| Authors | M. Ercsey-Ravasz, T. Roska, Z. Néda |
|---|---|
| Categories | |
| ArXiv ID | physics/0605108 |
| URL | https://arxiv.org/abs/physics/0605108 |
| DOI | 10.1140/epjb/e2006-00244-4 |
Abstract
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.
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"abstract": "The computational paradigm represented by Cellular Neural/nonlinear Networks\n(CNN) and the CNN Universal Machine (CNN-UM) as a Cellular Wave Computer, gives\nnew perspectives for computational physics. Many numerical problems and\nsimulations can be elegantly addressed on this fully parallelized and analogic\narchitecture. Here we study the possibility of performing stochastic\nsimulations on this chip. First a realistic random number generator is\nimplemented on the CNN-UM, and then as an example the two-dimensional Ising\nmodel is studied by Monte Carlo type simulations. The results obtained on an\nexperimental version of the CNN-UM with 128 * 128 cells are in good agreement\nwith the results obtained on digital computers. Computational time measurements\nsuggests that the developing trend of the CNN-UM chips - increasing the lattice\nsize and the number of local logic memories - will assure an important\nadvantage for the CNN-UM in the near future.",
"arxiv_id": "physics/0605108",
"authors": [
"M. Ercsey-Ravasz",
"T. Roska",
"Z. N\u00e9da"
],
"categories": [
"physics.comp-ph",
"physics.data-an"
],
"doi": "10.1140/epjb/e2006-00244-4",
"title": "Stochastic Simulations on the Cellular Wave Computers",
"url": "https://arxiv.org/abs/physics/0605108"
},
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