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dc.contributor.advisorSalama, Khaled N.
dc.contributor.authorAlahmadi, Hamzah
dc.date.accessioned2017-11-23T11:01:42Z
dc.date.available2017-11-23T11:01:42Z
dc.date.issued2017-10
dc.identifier.citationAlahmadi, H. (2017). Memristive Probabilistic Computing. KAUST Research Repository. https://doi.org/10.25781/KAUST-M506A
dc.identifier.doi10.25781/KAUST-M506A
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.
dc.language.isoen
dc.subjectMemristor
dc.subjectApproximate computing
dc.subjectAdder
dc.subjectIn-memory computing
dc.subjectProbabilistic computing
dc.subjectBeyond CMOS
dc.titleMemristive Probabilistic Computing
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHe, Jr-Hau
dc.contributor.committeememberGao, Xin
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.nameMaster of Science
refterms.dateFOA2018-06-13T14:29:31Z


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