PVF (Parameter Vulnerability Factor): A Quantitative Metric Measuring AI Vulnerability and Resilience Against Parameter Corruptions

arXiv:2405.01741v1 Announce Type: cross
Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them inevitably and increasingly susceptible to hardware faults (e.g., bit flips) that can potentially corrupt model parameters. Given this challenge, this paper aims to answer a critical question: How likely is a parameter corruption to result in an incorrect model output? To systematically answer this question, …

abstract adoption ai hardware ai systems ai technologies arxiv complexity cs.ai cs.ar cs.cr cs.lg deployment fundamental hardware measuring quantitative reliability resilience systems technologies them type vulnerability