Online Computing and Predicting Architectural Vulnerability Factor of Microprocessor Structures

Soft Errors have emerged as a key challenge to microprocessor design. Traditional soft error tolerance techniques (such as redundant multithreading and instruction duplication) can achieve high fault coverage but at the cost of significant performance degradation. Prior research reports that soft errors can be masked at the architecture level, and the degree of such masking, named as architecture vulnerability factor (AVF), can vary significantly across workloads and individual structures, hence strict redundant execution may not be necessary for soft error tolerance. In this work, we exploit the AVF varying feature to adaptively tune reliability and performance. We present an infrastructure to online compute and predict AVF for three microprocessor structures (IQ, ROB, and LSQ), guiding when the protection scheme should be activated to improve reliability. Experimental results show that our method can efficiently compute the AVF for different structures independent of hardware configurations. The average differences between our method and a prior offline AVF computing method are 0.10, 0.01, and 0.039 for IQ, ROB, and LSQ, respectively.