Configurable detection of SDC-causing errors in programs

Silent Data Corruption (SDC) is a serious reliability issue in many domains, including embedded systems. However, current protection techniques are brittle, and do not allow programmers to trade off performance for SDC coverage. Further, many of them require tens of thousands of fault injection experiments, which are highly time-intensive. In this paper, we propose two empirical models, namely SDCTune and SDCAuto, to predict the SDC proneness of a program’s data. Both models are based on static and dynamic features of the program alone, and do not require fault injections to be performed. We then develop an algorithm using both models to selectively protect the most SDC-prone data in the program subject to a given performance overhead bound. Our results show that both models are accurate at predicting the SDC rate of an application. And in terms of efficiency of detection (i.e., ratio of SDC coverage provided to performance overhead), our technique outperforms full duplication by a factor of 0.78x to 1.65x with SDCTune model, and 0.62x to 0.96x with SDCAuto model.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat