Fault-tolerant design of analogic cnn templates and algorithms-part i: the binary output case

Péter Földesy, L. Kék, Akos Zarândy

Research output: Contribution to journalArticle

16 Citations (Scopus)


This paper addresses the issue of designing a class of fault-tolerant cellular neural network (CNN) templates that, combined with CNN analogic algorithms, work correctly and reliably on given CNN universal machine (CNN-UM) chips. In particular, a generic method for finding nonpropagating binary-output CNN templates is proposed. This method is based on measurements of actual CNN-UM chips and combines adaptive optimization and decomposition of theoretically ideal CNN templates in order to correct the erroneous behavior of actual CNN-UM chips, which is mainly caused by imperfections introduced during fabrication. More specifically, the entire array of cells in a CNN-UM chip is modeled by a single feed-forward virtual cell whose optimal parameters are found by a simple and effective gradient-based method. In the case of binary input-output uncoupled templates (or Boolean operators), a systematic template decomposition method is applied whenever optimization fails to find a correctly working CNN template for all possible combinations of local.

Original languageEnglish
Pages (from-to)312-322
Number of pages11
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Issue number2
Publication statusPublished - Dec 1 1999



  • Boolean function decomposition
  • Cellular neural networks
  • Cnn universal machine (cnn-um)
  • Cnn-um chip
  • Template optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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