Fault tolerant CNN template design and optimization based on chip measurements

Peter Foldesy, Laszlo Kek, Tamas Roska, Akos Zarandy, Guszti Bartfai

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

This paper proposes a generic method for finding non-propagating Cellular Neural Network (CNN) templates that can be implemented reliably on a given CNN Universal Machine chip. The method has two main components: (i) adaptive optimization of templates based on measurements of actual CNN chips; (ii) simplification and decomposition of Boolean operators into a sequence of simpler ones that work correctly and more robustly on a given chip. Examples are presented using two concrete stored-program CNNUM chips to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

Original languageEnglish
Pages404-409
Number of pages6
Publication statusPublished - Jan 1 1998
EventProceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA - London, UK
Duration: Apr 14 1998Apr 17 1998

Other

OtherProceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA
CityLondon, UK
Period4/14/984/17/98

ASJC Scopus subject areas

  • Software

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    Foldesy, P., Kek, L., Roska, T., Zarandy, A., & Bartfai, G. (1998). Fault tolerant CNN template design and optimization based on chip measurements. 404-409. Paper presented at Proceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA, London, UK, .