Using likely program invariants to detect hardware errors

In the near future, hardware is expected to become increasingly vulnerable to faults due to continuously decreasing feature size. Software-level symptoms have previously been used to detect permanent hardware faults. However, they can not detect a small fraction of faults, which may lead to silent data corruptions(SDCs). In this paper, we present a system that uses invariants to improve the coverage and latency of existing detection techniques for permanent faults. The basic idea is to use training inputs to create likely invariants based on value ranges of selected program variables and then use them to identify faults at runtime. Likely invariants, however, can have false positives which makes them challenging to use for permanent faults. We use our on-line diagnosis framework for detecting false positives at runtime and limit the number of false positives to keep the associated overhead minimal. Experimental results using microarchitecture level fault injections in full-system simulation show 28.6% reduction in the number of undetected faults and 74.2% reduction in the number of SDCs over existing techniques, with reasonable overhead for checking code.