AI-based Timing Error Modelling: A Case Study on a Pipelined Floating-point Core

The adoption of aggressively down-scaled voltages along with worsening process variations render nanometer devices prone to timing errors that threaten system functionality [1] , [2] . Recent studies tried to predict timing errors using machine learning (ML), while considering some workload characteristics [3] , [4] , [5] . However, successfully training such models is challenging, since traditionally acquired samples are insufficient, especially in operating regions where timing errors occur rarely.