Dramatic increases in embedded data processing performance are becoming possible using platforms such as the NASA SpaceCube. With a flexible architecture and commercial devices, selected computations can be tuned for the highest performance while giving up perfect data reliability. More needs to be known about the nature of silent data corruption in this paradigm. When it occurs, how pervasive is it? To what extent can it be mitigated while near-optimal performance is maintained? This paper provides new insights into these questions, via a fault emulation-based study of two disparate applications running on a hard-core embedded processor. Two very low-cost methods of data error detection reduce the worst type of silent data corruption (SDC) by 89–97% with a performance overhead of ${<} 1hbox{%}$.
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