dorsal/arxiv
View SchemaPerspectives for Monte Carlo simulations on the CNN Universal Machine
| Authors | M. Ercsey-Ravasz, T. Roska, Z. Neda |
|---|---|
| Categories | |
| ArXiv ID | physics/0603121 |
| URL | https://arxiv.org/abs/physics/0603121 |
| DOI | 10.1142/S0129183106009230 |
Abstract
Possibilities for performing stochastic simulations on the analog and fully parallelized Cellular Neural Network Universal Machine (CNN-UM) are investigated. By using a chaotic cellular automaton perturbed with the natural noise of the CNN-UM chip, a realistic binary random number generator is built. As a specific example for Monte Carlo type simulations, we use this random number generator and a CNN template to study the classical site-percolation problem on the ACE16K chip. The study reveals that the analog and parallel architecture of the CNN-UM is very appropriate for stochastic simulations on lattice models. The natural trend for increasing the number of cells and local memories on the CNN-UM chip will definitely favor in the near future the CNN-UM architecture for such problems.
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"date_created": "2026-03-02T18:01:07.073000Z",
"date_modified": "2026-03-02T18:01:07.073000Z",
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"abstract": "Possibilities for performing stochastic simulations on the analog and fully\nparallelized Cellular Neural Network Universal Machine (CNN-UM) are\ninvestigated. By using a chaotic cellular automaton perturbed with the natural\nnoise of the CNN-UM chip, a realistic binary random number generator is built.\nAs a specific example for Monte Carlo type simulations, we use this random\nnumber generator and a CNN template to study the classical site-percolation\nproblem on the ACE16K chip. The study reveals that the analog and parallel\narchitecture of the CNN-UM is very appropriate for stochastic simulations on\nlattice models. The natural trend for increasing the number of cells and local\nmemories on the CNN-UM chip will definitely favor in the near future the CNN-UM\narchitecture for such problems.",
"arxiv_id": "physics/0603121",
"authors": [
"M. Ercsey-Ravasz",
"T. Roska",
"Z. Neda"
],
"categories": [
"physics.comp-ph",
"physics.data-an"
],
"doi": "10.1142/S0129183106009230",
"title": "Perspectives for Monte Carlo simulations on the CNN Universal Machine",
"url": "https://arxiv.org/abs/physics/0603121"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "565bd84e-70c8-4fb9-8da7-77a7d55c21bf",
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"type": "Model",
"variant": "snapshot-2026-03-01",
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