PVF: A novel metric for understanding AI systems’ vulnerability against SDCs in model parameters
Parameter vulnerability factor (PVF) is a new metric designed to measure AI systems' susceptibility to silent data corruptions in model parameters.
MAIN POINTS
- PVF measures AI systems' vulnerability to silent data corruptions (SDCs) in model parameters.
- It can be tailored to various AI models, tasks, and hardware faults.
- PVF can be extended to the training phase of AI models.
TAKEAWAYS
- PVF offers a novel approach to understanding AI vulnerability.
- It is adaptable to different AI models and hardware issues.
- The metric can be applied during AI model training.