TY - GEN
T1 - DVFS-Based scrubbing scheduling for reliability maximization on parallel tasks in SRAM-based FPGAs
AU - Li, Rui
AU - Yu, Heng
AU - Jiang, Weixiong
AU - Ha, Yajun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - To obtain high reliability but avoiding the huge area overhead of traditional triple modular redundancy (TMR) methods in SRAM-based FPGAs, scrubbing based methods reconfigure the configuration memory of each task just before its execution. However, due to the limitation of the FPGA reconfiguration module that can only scrub one task at a time, parallel tasks may leave stringent timing requirements to schedule their scrubbing processes. Thus the scrubbing requests may be either delayed or omitted, leading to a less reliable system. To address this issue, we propose a novel optimal DVFS-based scrubbing algorithm to adjust the execution time of user tasks, thus significantly enhance the chance to schedule scrubbing successfully for parallel tasks. Besides, we develop an approximation algorithm to speed up its optimal version and develop a novel K-Means based method to reduce the memory usage of the algorithm. Compared to the state-of-the-art, experimental results show that our work achieves up to 36.11% improvement on system reliability with comparable algorithm execution time and memory consumption.
AB - To obtain high reliability but avoiding the huge area overhead of traditional triple modular redundancy (TMR) methods in SRAM-based FPGAs, scrubbing based methods reconfigure the configuration memory of each task just before its execution. However, due to the limitation of the FPGA reconfiguration module that can only scrub one task at a time, parallel tasks may leave stringent timing requirements to schedule their scrubbing processes. Thus the scrubbing requests may be either delayed or omitted, leading to a less reliable system. To address this issue, we propose a novel optimal DVFS-based scrubbing algorithm to adjust the execution time of user tasks, thus significantly enhance the chance to schedule scrubbing successfully for parallel tasks. Besides, we develop an approximation algorithm to speed up its optimal version and develop a novel K-Means based method to reduce the memory usage of the algorithm. Compared to the state-of-the-art, experimental results show that our work achieves up to 36.11% improvement on system reliability with comparable algorithm execution time and memory consumption.
UR - http://www.scopus.com/inward/record.url?scp=85093969181&partnerID=8YFLogxK
U2 - 10.1109/DAC18072.2020.9218574
DO - 10.1109/DAC18072.2020.9218574
M3 - Conference contribution
AN - SCOPUS:85093969181
T3 - Proceedings - Design Automation Conference
BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -