Analog neural network based on memristor crossbar arrays

dc.contributor.author Yıldız, Hacer A.
dc.contributor.author Altun, Mustafa
dc.contributor.author Güngördü, Doğuş
dc.contributor.author Stan, Mircea
dc.contributor.department Elektronik ve Haberleşme Mühendisliği tr_TR
dc.contributor.department Electronics and Communications Engineering tr_TR
dc.date.accessioned 2020-01-28T10:00:31Z
dc.date.available 2020-01-28T10:00:31Z
dc.date.issued 2019
dc.description.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. tr_TR
dc.description.sponsorship 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 tr_TR
dc.description.version Author accepted manuscript(AMM)
dc.identifier.uri http://hdl.handle.net/11527/18204
dc.language.iso en tr_TR
dc.publisher İstanbul Teknik Üniversitesi tr_TR
dc.subject analog neural network en_US
dc.subject analog sinir ağı en_US
dc.subject memristor crossbar arrays en_US
dc.title Analog neural network based on memristor crossbar arrays en_US
dc.type Presentation tr_TR
dc.type Conference Paper tr_TR
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