Letter: A binary Hopfield neural network with hysteresis for large crossbar packet-switches

Authors:
Guangpu Xia;Zheng Tang;Yong Li;Jiahai Wang
Affiliations:
Faculty of Engineering, Toyama University, Toyama-shi 930-8555, Japan;Faculty of Engineering, Toyama University, Toyama-shi 930-8555, Japan;Faculty of Engineering, Toyama University, Toyama-shi 930-8555, Japan;Faculty of Engineering, Toyama University, Toyama-shi 930-8555, Japan
Venue:
Neurocomputing
Year:
2005

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Abstract

In this paper, we propose a hysteretic Hopfield neural network architecture for efficiently solving crossbar switch problems. A binary Hopfield neural network architecture with hysteresis binary neurons and its collective computational properties are studied. The network architecture is applied to a crossbar switch problem and results of computer simulations are presented and used to illustrate the computation power of the network architecture.