Experimental Verification of Uncoupled Memristive Cellular Nonlinear Network by Processing the EDGE Detection Task
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
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.
Details
| Original language | English |
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| Title of host publication | Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023 |
| Publisher | Association for Computing Machinery |
| ISBN (electronic) | 9798400703256 |
| Publication status | Published - 18 Dec 2023 |
| Peer-reviewed | Yes |
Publication series
| Series | Nanoarch: IEEE/ACM International Symposium on Nanoscale Architectures |
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Conference
| Title | 18th ACM International Symposium on Nanoscale Architectures |
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| Abbreviated title | NANOARCH 2023 |
| Conference number | 18 |
| Duration | 18 - 20 December 2023 |
| Website | |
| Location | Technische Universität Dresden |
| City | Dresden |
| Country | Germany |
External IDs
| ORCID | /0000-0001-7436-0103/work/154191788 |
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| ORCID | /0000-0002-2367-5567/work/168720267 |