Hardware realization of BSB recall function using memristor crossbar arrays

Authors:
Miao Hu;Hai Li;Qing Wu;Garrett S. Rose
Affiliations:
Polytechnic Institute of New York University, Metrotech Center, Brooklyn, NY;Polytechnic Institute of New York University, Metrotech Center, Brooklyn, NY;Air Force Research Laboratory, Rome Site, Rome, NY;Air Force Research Laboratory, Rome Site, Rome, NY
Venue:
Proceedings of the 49th Annual Design Automation Conference
Year:
2012

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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.