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dc.contributor.authorMansour, Wassimen_US
dc.contributor.authorVelazco, Raoulen_US
dc.contributor.authorAyoubi, Raficen_US
dc.description.abstractThe 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.en_US
dc.subjectRecurrent neural netsen_US
dc.subject.lcshFault-tolerant computingen_US
dc.titleFault-tolerance capabilities of a software-implemented Hopfield Neural Networken_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Communications and Information Technology (ICCIT) (3rd : 19-21 June 2013 : Beirut, Lebanon)en_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.relation.ispartoftext2013 Third International Conference on Communications and Information Technology (ICCIT)en_US
Appears in Collections:Department of Computer Engineering
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