Analog Neural Network based on Memristor Crossbar Arrays

Hacer A. Yildiz, Mustafa Altun, Ali Dogus Gungordu, Mircea R. Stan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, a new feed forward analog neural network is designed using a memristor based crossbar array architecture. This structure consists of positive and negative polarity connection matrices. In order to show the performance and usefulness of the proposed circuit, it is considered a sample application of iris data recognition. The proposed neural network implementation is approved by the simulation in Cadence design environment using 0.35μm CMOS technology. The results obtained are promising for the implementation of high density neural network.

Original languageEnglish
Title of host publicationELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages358-361
Number of pages4
ISBN (Electronic)9786050112757
DOIs
Publication statusPublished - Nov 2019
Event11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey
Duration: 28 Nov 201930 Nov 2019

Publication series

NameELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

Conference

Conference11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Country/TerritoryTurkey
CityBursa
Period28/11/1930/11/19

Bibliographical note

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

Funding

This work is part of a project that has received funding from the European Union’s H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement #691178

FundersFunder number
Horizon 2020 Framework Programme691178

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