NDLI: Vortex: Variation-aware training for memristor X-bar

Content Provider	IEEE Xplore Digital Library
Author	Beiye Liu Hai Li Yiran Chen Xin Li Qing Wu Tingwen Huang
Copyright Year	2015
Description	Author affiliation: Texas A&M Univ., Doha, Qatar (Tingwen Huang) \|\| ECE, Univ. of Pittsburgh, Pittsburgh, PA, USA (Beiye Liu; Hai Li; Yiran Chen) \|\| Air Force Res. Lab., Rome, NY, USA (Qing Wu) \|\| ECE, Carnegie Mellon Univ., Pittsburgh, PA, USA (Xin Li)
Abstract	Recent advances in development of memristor devices and cross-bar integration allow us to implement a low-power on-chip neuromorphic computing system (NCS) with small footprint. Training methods have been proposed to program the memristors in a crossbar by following existing training algorithms in neural network models. However, the robustness of these training methods has not been well investigated by taking into account the limits imposed by realistic hardware implementations. In this work, we present a quantitative analysis on the impact of device imperfections and circuit design constraints on the robustness of two popular training methods - “close-loop on-device” (CLD) and “open-loop off-device” (OLD). A novel variation-aware training scheme, namely, Vortex, is then invented to enhance the training robustness of memristor crossbar-based NCS by actively compensating the impact of device variations and optimizing the mapping scheme from computations to crossbars. On average, Vortex can significantly improve the test rate by 29.6% and 26.4%, compared to the traditional OLD and CLD, respectively.
Starting Page	1
Ending Page	6
File Size	667101
Page Count	6
File Format	PDF
ISBN	9781479980529
DOI	10.1145/2744769.2744930
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Memristors Training Resistance Programming Wires Sensors Switches
Content Type	Text
Resource Type	Article

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