Novel metric PVF assesses AI vulnerability to SDCs. #PVF

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

The content discusses the introduction of a novel metric called the parameter vulnerability factor (PVF) to measure AI systems’ vulnerability to silent data corruptions (SDCs) in model parameters. It emphasizes the importance of reliability in AI systems and the risks posed by hardware faults like bit flips. The article shares case studies using PVF to measure the impact of SDCs and methods to identify them in model parameters. PVF is described as a versatile metric that can be tailored to different AI models and tasks, adapted to hardware faults, and even extended to the training phase of AI models.

The article explains how PVF works, highlighting its features such as parameter-level quantitative assessment, scalability across AI models, and providing insights for guiding AI system design. It also discusses the applicability of PVF in evaluating AI vulnerability and resilience, standardizing practices, and fostering collaboration in the industry.

Furthermore, the content introduces Dr. DNA, a method designed to detect and mitigate SDCs during deep learning model inference. It presents results from an evaluation of Dr. DNA across various DNN models and tasks, showcasing its effectiveness in detecting and mitigating SDCs.

Overall, the content emphasizes the importance of understanding and mitigating the impact of SDCs on AI systems, introduces PVF as a valuable metric for assessing vulnerability, and presents Dr. DNA as a method for combating SDCs in deep learning.

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