Statistical physics on cellular neural network computers

M. Ercsey-Ravasz, T. Roska, Z. Néda

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 also for computational statistical physics. Thousands of locally interconnected cells working in parallel, analog signals giving the possibility of generating truly random numbers, continuity in time and the optical sensors included on the chip are just a few important advantages of such computers. Although CNN computers are mainly used and designed for image processing, here we argue that they are also suitable for solving complex problems in computational statistical physics. This study presents two examples of stochastic simulations on CNN: the site-percolation problem and the two-dimensional Ising model. Promising results are obtained using an ACE16K chip with 128×128 cells. In the second part of the work we discuss the possibility of using the CNN architecture in studying problems related to spin-glasses. A CNN with locally variant parameters is used for developing an optimization algorithm on spin-glass type models. Speed of the algorithms and further trends in developing the CNN chips are discussed.

Original languageEnglish
Pages (from-to)1226-1234
Number of pages9
JournalPhysica D: Nonlinear Phenomena
Volume237
Issue number9
DOIs
Publication statusPublished - Jul 1 2008

Fingerprint

Nonlinear networks
computer networks
Cellular neural networks
Statistical Physics
Cellular Networks
Physics
Neural Networks
physics
chips
Spin glass
spin glass
Chip
Spin Glass
random numbers
Ising model
optical measuring instruments
cells
continuity
Optical sensors
image processing

Keywords

  • Cellular neural network
  • Cellular wave computers
  • CNN universal machine
  • Unconventional computing

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistical and Nonlinear Physics

Cite this

Statistical physics on cellular neural network computers. / Ercsey-Ravasz, M.; Roska, T.; Néda, Z.

In: Physica D: Nonlinear Phenomena, Vol. 237, No. 9, 01.07.2008, p. 1226-1234.

Research output: Contribution to journalArticle

Ercsey-Ravasz, M. ; Roska, T. ; Néda, Z. / Statistical physics on cellular neural network computers. In: Physica D: Nonlinear Phenomena. 2008 ; Vol. 237, No. 9. pp. 1226-1234.
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