Multi-context scrubbing method

被引:0
|
作者
Fujimori, Takumi [1 ]
Watanabe, Minoru [1 ]
机构
[1] Shizuoka Univ, Elect & Elect Engn, 3-5-1 Johoku, Hamamatsu, Shizuoka 4328561, Japan
关键词
RECONFIGURABLE GATE ARRAY; MEMORY; SPEED;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Two salient concerns of current field programmable gate arrays (FPGAs) used for space applications are how to block soft errors that arise on their configuration memories and how to treat permanent failures attributable to total dose effects. To date, those two main concerns have been treated separately, but we present a proposal for multi-context scrubbing to "kill two birds with one stone" and resolve both issues simultaneously. To decrease the frequency of soft errors arising on the configuration memories of FPGAs, applying scrubbing operations for configuration memories is extremely useful. Since faster scrubbing can increase the radiation tolerances of the configuration memories on FPGAs, optical high-speed scrubbing using optically reconfigurable gate array (ORGA) architecture is introduced. Up to now, major scrubbing operations have invariably used a single configuration context, but since the storage capacities of holographic memories on ORGAs are high, many configuration contexts can be stored on a holographic memory. Thereby, various configuration contexts that avoid permanent failures can be used cyclically for scrubbing operations. Even if a permanent failure occurs on the programmable gate array during scrubbing operations, which exploit numerous configuration contexts, correct operations can be executed.
引用
收藏
页码:1548 / 1551
页数:4
相关论文
共 50 条
  • [41] Active Integrity Constraints for Multi-context Systems
    Cruz-Filipe, Luis
    Gaspar, Graca
    Nunes, Isabel
    Schneider-Kamp, Peter
    KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT, EKAW 2016, 2016, 10024 : 98 - 112
  • [42] Image Captioning with Multi-Context Synthetic Data
    Ma, Feipeng
    Zhou, Yizhou
    Rao, Fengyun
    Zhang, Yueyi
    Sun, Xiaoyan
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 5, 2024, : 4089 - 4097
  • [43] Saliency Detection by Multi-Context Deep Learning
    Zhao, Rui
    Ouyang, Wanli
    Li, Hongsheng
    Wang, Xiaogang
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 1265 - 1274
  • [44] Agent specification using multi-context systems
    Parsons, S
    Jennings, NR
    Sabater, J
    Sierra, C
    FOUNDATIONS AND APPLICATIONS OF MULTI-AGENT SYSTEMS, 2002, 2403 : 205 - 226
  • [45] Multi-Context Rewriting Induction with Termination Checkers
    Sato, Haruhiko
    Kurihara, Masahito
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2010, E93D (05) : 942 - 952
  • [46] Multi-context features for detecting malicious programs
    Saleh, Moustafa
    Li, Tao
    Xu, Shouhuai
    JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES, 2018, 14 (02): : 181 - 193
  • [47] Virtualizing hardware with multi-context reconfigurable arrays
    Enzler, R
    Plessl, C
    Platzner, M
    FIELD-PROGRAMMABLE LOGIC AND APPLICATIONS, PROCEEDINGS, 2003, 2778 : 151 - 160
  • [48] Minimal Change in Evolving Multi-Context Systems
    Goncalves, Ricardo
    Knorr, Matthias
    Leite, Joao
    PROGRESS IN ARTIFICIAL INTELLIGENCE-BK, 2015, 9273 : 611 - 623
  • [49] Distributed Evaluation of Nonmonotonic Multi-context Systems
    Dao-Tran, Minh
    Eiter, Thomas
    Fink, Michael
    Krennwallner, Thomas
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2015, 52 : 543 - 600
  • [50] HAGAR: Efficient multi-context graph processors
    Mencer, O
    Huang, ZN
    Huelsbergen, L
    FIELD-PROGRAMMABLE LOGIC AND APPLICATIONS, PROCEEDINGS: RECONFIGURABLE COMPUTING IS GOING MAINSTREAM, 2002, 2438 : 915 - 924