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Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications. |
| 개인저자 | Cai, Ermao. |
| 단체저자명 | Carnegie Mellon University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 135 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438079755 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Diana Marculescu. |
| 요약 | Energy and power are the main design constraints for modern high-performance computing systems. Indeed, energy efficiency plays a critical role in performance improvement or energy saving for either state-of-the-art general purpose hardware plat |
| 요약 | In this thesis, we study these effects and propose to combine machine learning techniques and domain knowledge to learn the performance, power, and energy models for high-performance computing systems. For technology-aware multi-core system desi |
| 일반주제명 | Computer engineering. Artificial intelligence. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
