Memristive Probabilistic Computing

Handle URI:
http://hdl.handle.net/10754/626192
Title:
Memristive Probabilistic Computing
Authors:
Alahmadi, Hamzah ( 0000-0003-3064-9671 )
Abstract:
In the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.
Advisors:
Salama, Khaled N. ( 0000-0001-7742-1282 )
Committee Member:
He, Jr-Hau ( 0000-0003-1886-9241 ) ; Gao, Xin ( 0000-0002-7108-3574 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Electrical Engineering
Issue Date:
Oct-2017
Type:
Thesis
Appears in Collections:
Theses

Full metadata record

DC FieldValue Language
dc.contributor.advisorSalama, Khaled N.en
dc.contributor.authorAlahmadi, Hamzahen
dc.date.accessioned2017-11-23T11:01:42Z-
dc.date.available2017-11-23T11:01:42Z-
dc.date.issued2017-10-
dc.identifier.urihttp://hdl.handle.net/10754/626192-
dc.description.abstractIn the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.en
dc.language.isoenen
dc.subjectMemristoren
dc.subjectApproximate computingen
dc.subjectAdderen
dc.subjectIn-memory computingen
dc.subjectProbabilistic computingen
dc.subjectBeyond CMOSen
dc.titleMemristive Probabilistic Computingen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen
dc.contributor.committeememberHe, Jr-Hauen
dc.contributor.committeememberGao, Xinen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.nameMaster of Scienceen
dc.person.id142376en
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