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dc.contributor.authorWorek, Brian Daviden_US
dc.date.accessioned2019-02-09T09:00:19Z
dc.date.available2019-02-09T09:00:19Z
dc.date.issued2019-02-08
dc.identifier.othervt_gsexam:18607en_US
dc.identifier.urihttp://hdl.handle.net/10919/87561
dc.description.abstractMemory capacity, bandwidth, and energy all continue to present hurdles in the quest for efficient, high-speed computing. Recognition, mining, and synthesis (RMS) applications in particular are limited by the efficiency of the memory subsystem due to their large datasets and need to frequently access memory. RMS applications, such as those in machine learning, deliver intelligent analysis and decision making through their ability to learn, identify, and create complex data models. To meet growing demand for RMS application deployment in battery constrained devices, such as mobile and Internet-of-Things, designers will need novel techniques to improve system energy consumption and performance. Fortunately, many RMS applications demonstrate inherent error resilience, a property that allows them to produce acceptable outputs even when data used in computation contain errors. Approximate storage techniques across circuits, architectures, and algorithms exploit this property to improve the energy consumption and performance of the memory subsystem through quality-energy scaling. This thesis reviews state of the art techniques in approximate storage and presents our own contribution that uses lossy compression to reduce the storage cost of media data.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectapproximate storageen_US
dc.subjectlossy compressionen_US
dc.subjectdata-intensiveen_US
dc.titleEnabling Approximate Storage through Lossy Media Data Compressionen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairAmpadu, Paul Ken_US
dc.contributor.committeememberHa, Dong S.en_US
dc.contributor.committeememberSchaumont, Patrick Roberten_US
dc.contributor.committeememberAthanas, Peter Men_US


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