Using Machine Learning Techniques to Evaluate Multicore Soft Error Reliability

Abstract

Virtual platform frameworks have been extended to allow earlier soft error analysis of more realistic multicore systems (i.e., real software stacks and state-of-the-art ISAs). The high observability and simulation performance of underlying frameworks enable to generate and collect more error/failure-related data, considering complex software stack configurations, in a reasonable time. When dealing with sizeable failure-related data sets obtained from multiple fault campaigns, it is essential to filter out parameters (i.e., features) without a direct relationship with the system soft error analysis. In this regard, this paper proposes the use of supervised and unsupervised machine learning techniques, aiming to eliminate non-relevant information as well as identify the correlation between fault injection results and application and platform characteristics. This novel approach provides engineers with appropriate means to investigate new and more efficient fault mitigation techniques. The underlying approach is validated with an extensive data set gathered from more than 1.2 million fault injections, comprising several benchmarks, a Linux OS and parallelization libraries (e.g., MPI and OpenMP), as well as through a realistic automotive case study.