Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation. (arXiv:2201.01588v1 [cs.LG])

The emergence of new nanoscale technologies has imposed significant
challenges to designing reliable electronic systems in radiation environments.
A few types of radiation like Total Ionizing Dose (TID) effects often cause
permanent damages on such nanoscale electronic devices, and current
state-of-the-art technologies to tackle TID make use of expensive
radiation-hardened devices. This paper focuses on a novel and different
approach: using machine learning algorithms on consumer electronic level Field
Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them …

anomaly detection arxiv detection learning machine machine learning