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Graphene memristive synapses for high precision neuromorphic computing

Authors :
Thomas F. Schranghamer
Aaryan Oberoi
Saptarshi Das
Source :
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Publication Year :
2020
Publisher :
Nature Portfolio, 2020.

Abstract

Designing efficient and low power memristors-based neuromorphic systems remains a challenge. Here, the authors present graphene-based multi-level (>16) and non-volatile memristive synapses with arbitrarily programmable conductance states capable of weight assignment based on k-means clustering.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.130631a23e64efc851890dbc7347d88
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-020-19203-z