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dc.contributor.advisorRaychowdhury, Arijit
dc.contributor.authorBhagi, Soundarya
dc.date.accessioned2020-01-14T14:49:11Z
dc.date.available2020-01-14T14:49:11Z
dc.date.created2019-12
dc.date.issued2019-12-05
dc.date.submittedDecember 2019
dc.identifier.urihttp://hdl.handle.net/1853/62362
dc.description.abstractThis research aims at modeling the effect of Roff to Ron ratio for a binary Resistive Random Access Memory (RRAM) based crossbar architecture with specific focus on deep learning application such as image classification. The crossbar structure uses emerging non-volatile memory (eNVM) array architecture and is simulated with complex neural networks to obtain metrics such as accuracy, inference and run-time. Model validation is performed by running benchmark image datasets. It will be possible to obtain other hardware results when this project is implemented on actual hardware.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectCrossbar array architecture
dc.subjectBinary RRAM
dc.subjectNeural networks
dc.subjectImage classification
dc.titleDesign of crossbar architecture for vector processing
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberYu, Shimeng
dc.contributor.committeeMemberKhan, Asif
dc.date.updated2020-01-14T14:49:11Z


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