GLAIVE: Graph Learning Assisted Instruction Vulnerability Estimation

Due to the continuous technology scaling and lowering of operating voltages, modern computer systems are highly vulnerable to soft errors induced by the high-energy particles. Soft errors can corrupt program outputs leading to silent data corruption or a Crash. To protect computer systems against such failures, architects need to precisely and quickly identify vulnerable program instructions that need to be protected. Traditional techniques for program reliability estimation either use expensive and time-consuming fault injection or inaccurate analytical models to identify the program instructions that need to be protected against soft errors. In this work, we present GLAIVE, a graph learning-assisted model for fast, accurate, and transferable soft-error induced instruction vulnerability estimation. GLAIVE leverages a synergy between static analysis and data-driven statistical reasoning to automatically learn signatures of instruction-level vulnerabilities and their propagation to program outputs using a fine-grain error propagation information from the bit-level program graphs of a set of realistic benchmarks. Our experiments show that the learned knowledge of instruction vulnerability is transferable to unseen programs. We further show that GLAIVE can achieve an average 221× speedup and up to 33.09 % lower program vulnerability estimation error as compared to a baseline fault-injection technique, up to 30.29 % higher vulnerability estimation accuracy, and on average can cover up to 90.23 % vulnerable instructions for a given protection budget compared to a set of baseline machine learning algorithms.