| LDR | | 02593nmm uu200445 4500 |
| 001 | | 000000334703 |
| 005 | | 20240805180839 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438066632 |
| 035 | |
▼a (MiAaPQ)AAI10746494 |
| 035 | |
▼a (MiAaPQ)usc:16003 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Li, Ji. |
| 245 | 10 |
▼a Improving Efficiency to Advance Resilient Computing. |
| 260 | |
▼a [S.l.] :
▼b University of Southern California.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 223 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Advisers: Jeffrey T. Draper |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Southern California, 2018. |
| 520 | |
▼a Resilience 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 |
| 520 | |
▼a Among 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 |
| 520 | |
▼a In 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 |
| 520 | |
▼a Accordingly, 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 |
| 520 | |
▼a In 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 |
| 590 | |
▼a School code: 0208. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer engineering. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0464 |
| 710 | 20 |
▼a University of Southern California.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0208 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996895
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |