Please use this identifier to cite or link to this item:
Title: Fault-tolerance capabilities of a software-implemented Hopfield Neural Network
Authors: Mansour, Wassim
Velazco, Raoul
Ayoubi, Rafic 
Affiliations: Department of Computer Engineering 
Keywords: Recurrent neural nets
Subjects: Fault-tolerant computing
Issue Date: 2013
Publisher: IEEE
Part of: 2013 Third International Conference on Communications and Information Technology (ICCIT)
Conference: International Conference on Communications and Information Technology (ICCIT) (3rd : 19-21 June 2013 : Beirut, Lebanon) 
The associative Hopfield memory is a form of recurrent Artificial Neural Network (ANN) that can be used in applications such as pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. In general, ANNs are considered as intrinsically fault-tolerant. A study of the capability of this algorithm to tolerate transient faults such as bit-flips provoked by the radiation environment is presented. Two software versions of the Hopfield Neural Network (HNN), one original and one fault-tolerant were implemented and executed by a LEON3 processor. Experimental results show the efficiency of the adopted strategy to tolerate faults that were injected at hardware level.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Computer Engineering

Show full item record

Record view(s)

checked on Oct 22, 2021

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.