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In-Database Machine Learning on Reconfigurable Dataflow Accelerators
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | In-Database Machine Learning on Reconfigurable Dataflow Accelerators. |
| 개인저자 | Vilim, Matthew. |
| 단체저자명 | Stanford University. |
| 발행사항 | [S.l.]: Stanford University., 2021. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2021. |
| 형태사항 | 120 p. |
| 기본자료 저록 | Dissertations Abstracts International 83-03A. Dissertation Abstract International |
| ISBN | 9798544204022 |
| 학위논문주기 | Thesis (Ph.D.)--Stanford University, 2021. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
Advisor: Olukotun, Oyekunle Ayinde; Hennessy, John L.; Re, Christopher. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 일반주제명 | Software. Communication. Bandwidths. Aggregates. Flexibility. Relational data bases. Databases. Design. Energy efficiency. Spectrum allocation. Algorithms. Queries. Field programmable gate arrays. Car pools. Information science. Data analysis. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
