PVF: A novel metric for understanding AI systems’ vulnerability against SDCs in model parameters

Introduces Parameter Vulnerability Factor (PVF), a novel metric to quantify AI model vulnerability against silent data corruptions (SDCs) in model parameters. PVF is derived from fault injection experiments and measures the probability that a corruption in a specific parameter leads to an incorrect output. The post details PVF's applicability across different models, tasks, and hardware fault models, and its extension to the training phase. It also highlights Dr. DNA, a method for detecting and mitigating SDCs during deep learning model inference, achieving high detection rates and performance recovery with low overhead.