Cellular neural networks with memristive cell devices

Gy Cserey*, Á Rák, B. Jákli, T. Prodromakis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present simulation measurements of a memristor crossbar device. We designed a PCB memristor package and the appropriate measurement board. Technical details of these circuits are presented. Cellular like topology of this crossbar device can provide high density and local connectivity. We gave a formula to evaluate the direction of the change of the states of the memristor array in case of a given voltage input. Our simulation results show that a memristor crossbar can be a trainable weight-matrix of a fully connected neural network if the memristors have ohmic non-linearity.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2010 - Proceedings
Pages938-941
Number of pages4
DOIs
Publication statusPublished - 7 Mar 2011
Event2010 IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2010 - Athens, Greece
Duration: 12 Dec 201015 Dec 2010

Publication series

Name2010 IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2010 - Proceedings

Conference

Conference2010 IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2010
Country/TerritoryGreece
CityAthens
Period12/12/1015/12/10

Keywords / Materials (for Non-textual outputs)

  • Cellular neural networks
  • Memristor

Fingerprint

Dive into the research topics of 'Cellular neural networks with memristive cell devices'. Together they form a unique fingerprint.

Cite this