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	Stochastic learning in oxide binary synaptic device for neuromorphic computing
	Yu, Shimeng ; Gao, Bin ; Fang, Zheng ; Yu, Hongyu ; Kang, Jinfeng ; Wong, H.S. Philip
	2013
关键词	resistive switching oxide RRAM synaptic device binary synapse neurommphic computing stochastic learning switching variability ARCHITECTURE PLASTICITY SYNAPSES SYSTEMS MEMORY
DOI	10.3389/fnins.2013.00186
英文摘要	Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.; Neurosciences; SCI(E); 7; ARTICLE; 7
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/392421]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yu, Shimeng,Gao, Bin,Fang, Zheng,et al. Stochastic learning in oxide binary synaptic device for neuromorphic computing[J],2013.
APA	Yu, Shimeng,Gao, Bin,Fang, Zheng,Yu, Hongyu,Kang, Jinfeng,&Wong, H.S. Philip.(2013).Stochastic learning in oxide binary synaptic device for neuromorphic computing..
MLA	Yu, Shimeng,et al."Stochastic learning in oxide binary synaptic device for neuromorphic computing".(2013).

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