NDLI: Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems

Content Provider	IEEE Xplore Digital Library
Author	Beiye Liu Hai Li Yiran Chen Xin Li Tingwen Huang Qing Wu Barnell, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Pittsburgh, Pittsburgh, PA, USA (Beiye Liu; Hai Li; Yiran Chen) \|\| Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA (Xin Li) \|\| Inf. Directorate, Air Force Res. Lab., Rome, NY, USA (Qing Wu; Barnell, M.) \|\| Texas A&M Univ. Doha, Doha, Qatar (Tingwen Huang)
Abstract	Neuromorphic computing system (NCS) is a promising architecture to combat the well-known memory bottleneck in Von Neumann architecture. The recent breakthrough on memristor devices made an important step toward realizing a low-power, small-footprint NCS on-a-chip. However, the currently low manufacturing reliability of nano-devices and the voltage IR-drop along metal wires and memristors arrays severely limits the scale of memristor crossbar based NCS and hinders the design scalability. In this work, we propose a novel system reduction scheme that significantly lowers the required dimension of the memristor crossbars in NCS while maintaining high computing accuracy. An IR-drop compensation technique is also proposed to overcome the adverse impacts of the wire resistance and the sneak-path problem in large memristor crossbar designs. Our simulation results show that the proposed techniques can improve computing accuracy by 27.0% and 38.7% less circuit area compared to the original NCS design.
Starting Page	63
Ending Page	70
File Size	2797233
Page Count	8
File Format	PDF
ISBN	9781479962785
DOI	10.1109/ICCAD.2014.7001330
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memristors Training Resistance Programming Neurons Vectors Robustness
Content Type	Text
Resource Type	Article

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