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dc.contributor.advisorDowning, Keithnb_NO
dc.contributor.authorVekterli, Tor Bredenb_NO
dc.date.accessioned2014-12-19T13:33:55Z
dc.date.available2014-12-19T13:33:55Z
dc.date.created2010-09-04nb_NO
dc.date.issued2009nb_NO
dc.identifier348757nb_NO
dc.identifierntnudaim:4267nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/251334
dc.description.abstractConventional artificial neural networks have traditionally faced inherent problems with efficient parallelization of neuron processing. Recent research has shown how artificial spiking neural networks can, with the introduction of biologically plausible synaptic conduction delays, be fully parallelized regardless of their network topology. This, in conjunction with the influx of fast, massively parallel desktop-level computing hardware leaves the field of efficient, large-scale spiking neural network simulations potentially open to even those with no access to supercomputers or large computing clusters. This thesis aims to show how such a parallelization is possible as well as present a network model that enables it. This model will then be used as a base for implementing a parallel artificial spiking neural network on both the CPU and the GPU and subsequently evaluating some of the challenges involved, the performance and scalability measured and the potential that is exhibited.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaimno_NO
dc.subjectMIT informatikkno_NO
dc.subjectKunstig intelligens og læringno_NO
dc.titleParallelization of Artificial Spiking Neural Networks on the CPU and GPUnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber83nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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