BoostIID: Fault-agnostic Online Detection of WCET Changes in Autonomous Driving
Saehanseul Yi, Nikil D. Dutt · 2024
The lifespan of autonomous vehicles is increasing, exposing them to aging and permanent faults that can impact timing safety based on design-time analyses such as worst-case execution time (WCET). Conventional fault-aware WCET methods incorporate potential faults into the analysis, which can result in severely pessimistic WCET estimates. The resulting underutilized system can exhibit significant energy inefficiency during normal operation. We propose BoostIID, a dynamic fault detection mechanism that achieves improved utilization and energy efficiency by monitoring changes in the statistical distribution of WCET at runtime. Unlike prior monitoring methods, BoostIID proactively detects faults before actual timing violations occur, allowing time for recovery measures. As a result, BoostIID eliminates the overhead of fault recovery from classical design-time WCET estimates, resulting in improved energy efficiency. We further improve the detection accuracy using a collection of independent and identically distributed (i.i.d) tests with a boosting technique. Our experimental results with an autonomous driving benchmark suite show 62.6% energy reduction over pessimistic WCET methods, demonstrating BoostIID’s utility for energy-efficient safety-critical system design.