  Radiation-induced soft errors are one of 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 paper addresses the promising field of
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 dedicated Performance Monitoring Unit (PMU). However, there is
an open question to understand to what extent they are enough to detect faulty
executions. Exploiting the capability of gem5 to simulate real computing
systems, perform fault injection experiments and profile microarchitectural
attributes (i.e., gem5 Stats), this paper presents the results of a
comprehensive analysis regarding the potential attributes to detect soft error
and the associated models that can be trained with these features.
