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

Guangpu Xia; Zheng Tang; Yong Li; Jiahai Wang

doi:10.1016/j.neucom.2004.09.004

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

Guangpu Xia^*, Zheng Tang, Yong Li, Jiahai Wang

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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.

Original language	English
Pages (from-to)	417-425
Number of pages	9
Journal	Neurocomputing
Volume	67
Issue number	1-4 SUPPL.
DOIs	https://doi.org/10.1016/j.neucom.2004.09.004
State	Published - 2005/08

Keywords

Collective computational properties
Crossbar switch
Hysteresis
Network architecture

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2004.09.004

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title = "A binary Hopfield neural network with hysteresis for large crossbar packet-switches",

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.",

keywords = "Collective computational properties, Crossbar switch, Hysteresis, Network architecture",

author = "Guangpu Xia and Zheng Tang and Yong Li and Jiahai Wang",

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AU - Tang, Zheng

AU - Li, Yong

AU - Wang, Jiahai

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Y1 - 2005/8

N2 - 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.

AB - 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.

KW - Collective computational properties

KW - Crossbar switch

KW - Hysteresis

KW - Network architecture

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

Abstract

Keywords

ASJC Scopus subject areas

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