### 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 language | English |
---|---|

Pages (from-to) | 1226-1234 |

Number of pages | 9 |

Journal | Physica D: Nonlinear Phenomena |

Volume | 237 |

Issue number | 9 |

DOIs | |

Publication status | Published - Jul 1 2008 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Applied Mathematics
- Statistical and Nonlinear Physics

### Cite this

*Physica D: Nonlinear Phenomena*,

*237*(9), 1226-1234. https://doi.org/10.1016/j.physd.2008.03.028

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

Research output: Contribution to journal › Article

*Physica D: Nonlinear Phenomena*, vol. 237, no. 9, pp. 1226-1234. https://doi.org/10.1016/j.physd.2008.03.028

}

TY - JOUR

T1 - Statistical physics on cellular neural network computers

AU - Ercsey-Ravasz, M.

AU - Roska, T.

AU - Néda, Z.

PY - 2008/7/1

Y1 - 2008/7/1

N2 - 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.

AB - 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.

KW - Cellular neural network

KW - Cellular wave computers

KW - CNN universal machine

KW - Unconventional computing

UR - http://www.scopus.com/inward/record.url?scp=44349119685&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44349119685&partnerID=8YFLogxK

U2 - 10.1016/j.physd.2008.03.028

DO - 10.1016/j.physd.2008.03.028

M3 - Article

AN - SCOPUS:44349119685

VL - 237

SP - 1226

EP - 1234

JO - Physica D: Nonlinear Phenomena

JF - Physica D: Nonlinear Phenomena

SN - 0167-2789

IS - 9

ER -