PFS - Resiliency to Soft-Errors for Embedded Processors Using ML-based Checkers

With deep submicron scaling, soft error has become one of the major reliability challenges for electronic systems. This work proposes a Machine Learning-based Checker (MLC) to protect hard-core processors against radiation-induced soft errors. MLC is an independent hardware unit that implements an ML algorithm to detect soft errors in a processor and works in a parallel fashion with that. This work considers XGBoost as a lightweight and high-performance machine learning method. XGBoost is trained based on some programmers’ view features of the processor like control signals. The proposed scheme is applied to a RISC-V-like processor, called SAYAC, as a case study