Silent Data Corruption Estimation and Mitigation Without Fault Injection

Silent data corruptions (SDCs) have been always regarded as the serious effect of radiation-induced faults. Traditional solutions based on redundancies are very expensive in terms of chip area, energy consumption, and performance. Consequently, providing low-cost and efficient approaches to cope with SDCs has received researchers' attention more than ever. On the other hand, identifying SDC-prone data and instruction in a program is a very challenging issue, as it requires time-consuming fault injection processes into different parts of a program. In this article, we present a cost-efficient approach to detecting and mitigating the rate of SDCs in the whole program with the presence of multibit faults without a fault injection process. This approach uses a combination of machine learning and a metaheuristic algorithm that predicts the SDC event rate of each instruction. The evaluation results show that the proposed approach provides a high level of detection accuracy of 99% while offering a low-performance overhead of 58%.