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Checkpoint placement for systematic fault-injection campaigns
Citation Link: https://doi.org/10.15480/882.8901
Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
TORE-DOI
Citation
IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2023)
Contribution to Conference
Publisher DOI
Scopus ID
ArXiv ID
Shrinking hardware structures and decreasing operating voltages lead to an increasing number of transient hardware faults, which thus become a core problem to consider for safety-critical systems. Here, systematic fault injection (FI), where one program under-test is systematically stressed with faults, provides an in-depth resilience analysis in the presence of faults. However, FI campaigns require many independent injection experiments and, combined, long run times, especially if we aim for a high coverage of the fault space. One cost factor is the forwarding phase, which is the time required to bring the system-under test into the fault-free state at injection time. One common technique to speed up the forwarding are checkpoints of the fault-free system state at fixed points in time.
In this paper, we show that the placement of checkpoints has a significant influence on the required forwarding cycles, especially if we place faults non-uniformly on the time axis. For this, we discuss the checkpoint-selection problem in general, formalize it as a maximum-weight reward path problem in graphs, propose an ILP formulation and a dynamic programming algorithm that find the optimal solution, and provide a heuristic checkpoint-selection method based on a genetic algorithm. Applied to the MiBench benchmark suite, our approach consistently reduces the forward-phase cycles by at least 88 percent and up to 99.934 percent when placing 16 checkpoints.
In this paper, we show that the placement of checkpoints has a significant influence on the required forwarding cycles, especially if we place faults non-uniformly on the time axis. For this, we discuss the checkpoint-selection problem in general, formalize it as a maximum-weight reward path problem in graphs, propose an ILP formulation and a dynamic programming algorithm that find the optimal solution, and provide a heuristic checkpoint-selection method based on a genetic algorithm. Applied to the MiBench benchmark suite, our approach consistently reduces the forward-phase cycles by at least 88 percent and up to 99.934 percent when placing 16 checkpoints.
Subjects
Checkpoint Placement
Fault Injection
DDC Class
510: Mathematics
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