Handle URI:
http://hdl.handle.net/10754/624804
Title:
Neuro-Inspired Computing with Stochastic Electronics
Authors:
Naous, Rawan ( 0000-0001-6129-7926 ) ; Al-Shedivat, Maruan ( 0000-0001-9037-1005 ) ; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N. ( 0000-0001-7742-1282 )
Abstract:
The 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
6-Jan-2016
Type:
Poster
Appears in Collections:
Posters; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

Full metadata record

DC FieldValue Language
dc.contributor.authorNaous, Rawanen
dc.contributor.authorAl-Shedivat, Maruanen
dc.contributor.authorNeftci, Emreen
dc.contributor.authorCauwenberghs, Gerten
dc.contributor.authorSalama, Khaled N.en
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.en
dc.subjectSDEen
dc.titleNeuro-Inspired Computing with Stochastic Electronicsen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
dc.contributor.institutionUniversity of California San Diegoen
kaust.authorNaous, Rawanen
kaust.authorAl-Shedivat, Maruanen
kaust.authorSalama, Khaled N.en
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