Analysis of the Memristor-Based Crossbar Synapse for Neuromorphic Systems
- 주제(키워드) Neuromorphic System , Memristor , Crossbar Architecture , Machine Learning , Guide Training
- 주제(기타) Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter
- 설명문(일반) [Kim, Bokyung; Jo, Sumin; Sun, Wookyung; Shin, Hyungsoon] Ewha Womans Univ, Dept Elect & Elect Engn, 11-1 Daehyun Dong, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 AMER SCIENTIFIC PUBLISHERS
- 발행년도 2019
- URI http://www.dcollection.net/handler/ewha/000000159703
- 본문언어 영어
- Published As http://dx.doi.org/10.1166/jnn.2019.17110
- PubMed https://pubmed.ncbi.nlm.nih.gov/31027014
초록/요약
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.
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