NDLI: Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

Content Provider	IEEE Xplore Digital Library
Author	Miao Hu Hai Li Yiran Chen Qing Wu Rose, G.S. Linderman, R.W.
Copyright Year	2012
Abstract	By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue.
Starting Page	1864
Ending Page	1878
Page Count	15
File Size	2629898
File Format	PDF
ISSN	2162237X
Volume Number	25
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memristors Training Arrays Hardware Neuromorphics Biological neural networks Neurons neuromorphic hardware. Associative memory brain-state-in-a-box (BSB) crossbar array memristor neuromorphic hardware
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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