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Approximate Binary Decision Diagrams for High-Performance Computing
| Title: | Approximate Binary Decision Diagrams for High-Performance Computing. |
 42 views 20 downloads |
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| Name(s): |
Sivakumar, Anagha, Author Jha, Sumit Kumar, Committee Chair Leavens, Gary, Committee Member Valliyil Thankachan, Sharma, Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2018 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | 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 diagrams. Our work may be useful in designing massively-parallel approximate crossbar computing systems for application-specific domains such as probabilistic computational modeling. | |
| Identifier: | CFE0007414 (IID), ucf:52704 (fedora) | |
| Note(s): |
2018-05-01 M.S. Engineering and Computer Science, Computer Science Masters This record was generated from author submitted information. |
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| Subject(s): | Binary decision diagrams -- approximate computing -- high-performance computing -- flow-based computing -- crossbar computing | |
| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0007414 | |
| Restrictions on Access: | campus 2021-11-15 | |
| Host Institution: | UCF |
