Effects of Online Fault Detection Mechanisms on Probabilistic Timing Analysis

In real time systems, random caches have been proposed as a way to simplify software timing analysis, by avoiding corner cases usually found in deterministic systems. Using this random approach, one can obtain an application's probabilistic Worst Case Execution Time (pWCET) to be used for timing analysis. As with deterministic systems, technology scaling in cache memories is making transient and permanent faults more likely, which in turn affects the system's timing behavior. To mitigate these effects, one can introduce a detection mechanism that classifies a fault as transient or permanent, with the goal of disabling permanently faulty cache blocks to avoid future accesses. In this paper, we compare the effects of two online detection mechanisms for permanent faults, namely rule-based detection and Dynamic Hidden Markov Model (D-HMM) based detection, for the generation of safe pWCET estimates. Experimental results show that different mechanisms can greatly affect safe pWCET margins, and that by using D-HMM the pWCET of the system can be improved compared to rule-based detection.