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dc.contributor.authorNaous, Rawan
dc.contributor.authorAl-Shedivat, Maruan
dc.contributor.authorNeftci, Emre
dc.contributor.authorCauwenberghs, Gert
dc.contributor.authorSalama, Khaled N.
dc.date.accessioned2017-06-08T06:32:27Z
dc.date.available2017-06-08T06:32:27Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624804
dc.description.abstractThe extensive scaling and integration within electronic systems have set the standards for what is addressed to as stochastic electronics. The individual components are increasingly diverting away from their reliable behavior and producing un-deterministic outputs. This stochastic operation highly mimics the biological medium within the brain. Hence, building on the inherent variability, particularly within novel non-volatile memory technologies, paves the way for unconventional neuromorphic designs. Neuro-inspired networks with brain-like structures of neurons and synapses allow for computations and levels of learning for diverse recognition tasks and applications.
dc.subjectSDE
dc.titleNeuro-Inspired Computing with Stochastic Electronics
dc.typePoster
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionUniversity of California San Diego
kaust.personNaous, Rawan
kaust.personAl-Shedivat, Maruan
kaust.personSalama, Khaled N.
refterms.dateFOA2018-06-14T06:26:29Z


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