Current Search: Sivakumar, Anagha (x)
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Title
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Approximate Binary Decision Diagrams for High-Performance Computing.
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Creator
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Sivakumar, Anagha, Jha, Sumit Kumar, Leavens, Gary, Valliyil Thankachan, Sharma, University of Central Florida
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Abstract / Description
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Many soft applications such as machine learning and probabilistic computational modeling can benefit from approximate but high-performance implementations. In this thesis, we study how Binary decision diagrams (BDDs) can be used to synthesize approximate high-performance implementations from high-level specifications such as program kernels written in a C-like language. We demonstrate the potential of our approach by designing nanoscale crossbars from such approximate Boolean decision...
Show moreMany soft applications such as machine learning and probabilistic computational modeling can benefit from approximate but high-performance implementations. In this thesis, we study how Binary decision diagrams (BDDs) can be used to synthesize approximate high-performance implementations from high-level specifications such as program kernels written in a C-like language. We demonstrate the potential of our approach by designing nanoscale crossbars from such approximate Boolean decision diagrams. Our work may be useful in designing massively-parallel approximate crossbar computing systems for application-specific domains such as probabilistic computational modeling.
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Date Issued
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2018
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Identifier
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CFE0007414, ucf:52704
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0007414
