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Checkpoint placement for systematic fault-injection campaigns
Citation Link: https://doi.org/10.15480/882.8901
Publikationstyp
Conference Paper
Publikationsdatum
2023
Sprache
English
Citation
IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2023)
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Publisher DOI
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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.
Schlagworte
Checkpoint Placement
Fault Injection
DDC Class
510: Mathematics
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