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Analysis of the Memristor-Based Crossbar Synapse for Neuromorphic Systems

Title
Analysis of the Memristor-Based Crossbar Synapse for Neuromorphic Systems
Authors
Kim, BokyungJo, SuminSun, WookyungShin, Hyungsoon
Ewha Authors
신형순선우경
SCOPUS Author ID
신형순scopus
Issue Date
2019
Journal Title
JOURNAL OF NANOSCIENCE AND NANOTECHNOLOGY
ISSN
1533-4880JCR Link

1533-4899JCR Link
Citation
JOURNAL OF NANOSCIENCE AND NANOTECHNOLOGY vol. 19, no. 10, pp. 6703 - 6709
Keywords
Neuromorphic SystemMemristorCrossbar ArchitectureMachine LearningGuide Training
Publisher
AMER SCIENTIFIC PUBLISHERS
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
In this study, we analyzed the memristor device typically used as a synapse in neuromorphic architecture and confirmed that the synaptic memristor device can be adopted to perform the machine learning algorithm. The nonlinear characteristics of the memristor complicates its use as the neuromorphic hardware in an artificial neural network (ANN) with a back-propagation algorithm. Using a memristor device with a nonlinear characteristic, we demonstrated that pattern classification can be implemented in ANNs using the Guide training algorithm without back-propagation. Furthermore, the memristor characteristics required to achieve accurate learning results are analyzed.
DOI
10.1166/jnn.2019.17110
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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