Technology-aware system failure analysis in the presence of soft errors by Mixture Importance Sampling

This paper proposes a fault injection method for the accurate prediction of failure rates of embedded applications. The presented approach relies on Mixture Importance Sampling. Hence, it is very efficient and requires far fewer samples than standard Monte Carlo. We utilize the presented injection method to link a technology-aware fault model for cache soft errors to a system-level simulation. This cross-layer approach is demonstrated to analyze the fault tolerance of an autonomous robot. The presented approach is a step towards designing fault tolerant embedded systems with reduced protection mechanisms to save power and area, as this requires efficient methods to predict system failure rates.