NDLI: Hardware realization of BSB recall function using memristor crossbar arrays

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Hardware realization of BSB recall function using memristor crossbar arrays

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Hardware realization of BSB recall function using memristor crossbar arrays

Content Provider	ACM Digital Library
Author	Hu, Miao Li, Hai Wu, Qing Rose, Garrett S.
Abstract	The Brain-State-in-a-Box (BSB) model is an auto-associative neural network that has been widely used in optical character recognition and image processing. Traditionally, the BSB model was realized at software level and carried out on high-performance computing clusters. To improve computation efficiency and reduce resources requirement, we propose a hardware realization by utilizing memristor crossbar arrays. In this work, we explore the potential of a memristor crossbar array as an auto-associative memory. More specificly, the recall function of a multi-answer character recognition based on BSB model was realized. The robustness of the proposed BSB circuit was analyzed and evaluated based on massive Monte-Carlo simulations, considering input defects, process variations, and electrical fluctuations. The physical constrains when implementing a neural network with memristor crossbar array have also been discussed. Our results show that the BSB circuit has a high tolerance to random noise. Comparably, the correlations between memristor arrays introduces directional noise and hence dominates the quality of circuits.
Starting Page	498
Ending Page	503
Page Count	6
File Format	PDF
ISBN	9781450311991
ISSN	85644924
DOI	10.1145/2228360.2228448
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bsb model Memristor Crossbar array Neural network Process variation
Content Type	Text
Resource Type	Article

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