NDLI: Digital-assisted noise-eliminating training for memristor crossbar-based analog neuromorphic computing engine

Content Provider	IEEE Xplore Digital Library
Author	Beiye Liu Miao Hu Hai Li Zhi-Hong Mao Yiran Chen Tingwen Huang Wei Zhang
Copyright Year	2013
Description	Author affiliation: Nanyang Technol. Univ., Singapore, Singapore (Wei Zhang) \|\| Texas A&M Univ., Doha, Qatar (Tingwen Huang) \|\| Univ. of Pittsburgh, Pittsburgh, PA, USA (Beiye Liu; Miao Hu; Hai Li; Zhi-Hong Mao; Yiran Chen)
Abstract	The invention of neuromorphic computing architecture is inspired by the working mechanism of human-brain. Memristor technology revitalized neuromorphic computing system design by efficiently executing the analog Matrix-Vector multiplication on the memristor-based crossbar (MBC) structure. However, programming the MBC to the target state can be very challenging due to the difficulty to real-time monitor the memristor state during the training. In this work, we quantitatively analyzed the sensitivity of the MBC programming to the process variations and input signal noise. We then proposed a noise-eliminating training method on top of a new crossbar structure to minimize the noise accumulation during the MBC training and improve the trained system performance, i.e.,the pattern recall rate. A digital-assisted initialization step for MBC training is also introduced to reduce the training failure rate as well as the training time. Experimental results show that our noise-eliminating training method can improve the pattern recall rate. For the tested patterns with 128 × 128 pixels our technique can reduce the MBC training time by 12.6% ~ 14.1% for the same pattern recognition rate, or improve the pattern recall rate by 18.7% ~ 36.2% for the same training time.
Starting Page	1
Ending Page	6
File Size	2160500
Page Count	6
File Format	PDF
ISBN	9781450320719
ISSN	0738100X
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Abstracts Lead Training Memristors Engines Registers Pattern recogniton Memristor Neuromorphic Computing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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