Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators. (arXiv:2212.02649v1 [cs.AR])

As Deep Neural Networks (DNNs) are increasingly deployed in safety critical
and privacy sensitive applications such as autonomous driving and biometric
authentication, it is critical to understand the fault-tolerance nature of
DNNs. Prior work primarily focuses on metrics such as Failures In Time (FIT)
rate and the Silent Data Corruption (SDC) rate, which quantify how often a
device fails. Instead, this paper focuses on quantifying the DNN accuracy given
that a transient error has occurred, which tells us how well …

thales vulnerability