Experimental Verification of Uncoupled Memristive Cellular Nonlinear Network by Processing the EDGE Detection Task

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The Cellular Nonlinear Network (CNN) is a powerful paradigm in analog computing. As pure-CMOS based CNN Universal Machine faces the von Neumann bottleneck, the integration of memristive devices with their non-volatile memory properties is of major interest. These networks are called Memristor-CNNs (M-CNNs). Moreover, the integration of memristors brings richer dynamics into the network, such that M-CNNs are highly suitable for neuromorphic computing tasks. This paper presents the experimental verification of a processing unit of an uncoupled M-CNN design with a valance change mechanism (VCM) based memristor. We outline a simple measurement strategy to study M-CNNs with real-world devices and provide compelling evidence that the results of the M-CNN processing element are stored in a non-volatile manner. This work further offers crucial insights into design considerations of M-CNN networks.


Original languageEnglish
Title of host publicationProceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023
PublisherAssociation for Computing Machinery
ISBN (electronic)9798400703256
Publication statusPublished - 18 Dec 2023

Publication series

SeriesACM International Conference Proceeding Series


Title18th ACM International Symposium on Nanoscale Architectures
Abbreviated titleNANOARCH 2023
Conference number18
Duration18 - 20 December 2023
LocationTechnische Universität Dresden

External IDs

ORCID /0000-0001-7436-0103/work/154191788