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Improving Efficiency to Advance Resilient Computing

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개인저자Li, Ji.
단체저자명University of Southern California. Electrical Engineering.
서명/저자사항Improving Efficiency to Advance Resilient Computing.
발행사항[S.l.] : University of Southern California., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항223 p.
소장본 주기School code: 0208.
ISBN9780438066632
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisers: Jeffrey T. Draper
요약Resilience is a major roadblock for high-performance computing (HPC) executions on future exascale systems, as the increased likelihood of much higher error rates results in systems that fail frequently and make little progress in computations o
요약Among all the hardware failure mechanisms, radiation-induced soft errors have become one of the most challenging issues [KMH12, WDT+14], which can lead to silent data corruptions and system failures, with potentially disastrous results in missio
요약In the process, Deep Neural Network (DNN) and Deep Convolutional Neural Network (DCNN) have emerged as high performance resilient systems, which completely tolerate radiation-induced soft errors. More importantly, DNN and DCNN have achieved brea
요약Accordingly, the second part of this thesis is dedicated to solve the aforementioned challenges. A Deep Reinforcement Learning (DRL)-based framework is proposed, which utilizes the resilient DNNs together with the reinforcement learning method t
요약In conclusion, this thesis is dedicated to improving the efficiency of resilient computing through both a classical approach, i.e., fast and comprehensive SER evaluation framework for conventional computing circuits, and another novel approach i
일반주제명Electrical engineering.
Computer engineering.
언어영어
기본자료 저록Dissertation Abstracts International79-10B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14996895

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