Towards analyzing and improving robustness of software applications to intermittent and permanent faults in hardware

Although a significant fraction of emerging failure and wearout mechanisms result in intermittent or permanent faults in hardware, their impact (as distinct from transient faults) on software applications has not been well studied. In this paper, we develop a distinguishing application characteristic, referred to as similarity from fundamental circuit-level understanding of the failure mechanisms. We present a mathematical definition and a procedure for similarity computation for practical software applications and experimentally verify the relationship between similarity and fault rate. Leveraging dependence of application robustness on the similarity metric, we present example architecture independent code transformations to reduce similarity and thereby the worst-case fault rate with minimal performance degradation. Our experimental results with arithmetic unit faults show as much as 74% improvement in the worst case fault rate on benchmark kernels, with less than 10% runtime penalty.