Identify Silent Data Corruption Vulnerable Instructions Using SVM

Abstract

Silent data corruption (SDC) is the most insidious and harmful result type of soft error. Identify program vulnerable instructions (PVIns) that are likely to cause SDCs is extremely significant on selective software-based protection techniques. However, current identification techniques require tremendous fault injections or have non-negligible differences in performance among different programs as well as different program inputs. This paper proposes PVInsiden to reduce the cost of fault injection and improve the adaptability for programs and program inputs. Machine learning is used to learn a classifier which predicts whether an instruction is a PVIns. Partial fault injection is applied to generate a training dataset, reducing the cost of fault injection. The feature engineering, including selecting features and transforming the selected features into quantifiable representations is explored. Furthermore, the framework of learning the classifier is given. The experimental results show that PVInsiden only uses 35% fault injections to identify 85% PVIns with 80% precision, reducing the cost of fault injection efficiently. PVInsiden also shows high performance of precision, recall, and f0.5-score for different programs as well as different program inputs.