AI-based soft error detection for embedded applications

Radiation-induced soft errors are among the most challenging issues in Safety-Critical Real-Time Embedded System (SACRES) reliability, usually handled using different flavors of Double Modular Redundancy (DMR) techniques. This solution is becoming unaffordable due to the complexity of modern micro-processors in all domains. This thesis investigates using Artificial Intelligence (AI) based hardware detectors for soft errors. To create such cores and make them general enough to work with different software applications, microarchitectural attributes are a fascinating option as candidate fault detection features. Several processors already track these features through the dedicated Performance Monitoring Unit (PMU). However, there is an open question to understand to what extent they are enough to detect faulty executions. This thesis will investigate possible solutions.