Rapid Fault Injection Simulation by Hash-Based Differential Fault Effect Equivalence Checks

Abstract

Assessing a computational system's resilience to hardware faults is essential for safety and security-related systems. Fault Injection (FI) simulation is a valuable tool that can increase confidence in computational systems and guide hardware and software design decisions in the early stages of development. However, simulating hardware at low levels of abstraction, such as Register Transfer Level (RTL), is costly, and minimizing the effort required for large-scale FI campaigns is a significant objective. This work introduces Hash-based Differential Fault Effect Equivalence Checks to automatically terminate experiments early based on predicting their outcome. We achieve this by matching observed fault effects to ones already encountered in previous experiments. We generate these hashes from differentials computed by repurposing existing fast boot checkpoints from a state-of-the-art acceleration method. By integrating these approaches in an automated manner, we can accelerate a large-scale FI simulation of a CPU at RTL. We reduce the average simulation time by a factor of up to 25 compared to a factor of around 2 to 5 for state-of-the-art techniques. While maintaining 100 % accuracy, we can recover the faulty state through the stored differentials.