SLoT: A Supervised Learning Model to Predict Dynamic Timing Errors of Functional Units

Dynamic timing errors (DTEs), that are caused by the timing violations of sensitized critical timing paths, have emerged as an important threat to the reliability of digital circuits. Existing approaches model the DTEs without considering the impact of input operands on dynamic path sensitization, resulting in loss of accuracy. The diversity of input operands leads to complex path sensitization behaviors, making it hard to represent in DTE modeling. In this paper, we propose SLoT, a supervised learning model to predict the output of functional units (FUs) to be one of two timing classes: {timing correct, timing erroneous} as a function of input operands and clock period. We apply random forest classification (RFC) method to construct SLoT, by using input operands, computation history and circuit toggling as input features and outputs' timing classes as labels. The outputs' timing classes are measured using gate-level simulation (GLS) of a post place-and-route design in TSMC 45nm process. For evaluation, we apply SLoT to several FUs and on average 95% predictions are consistent with GLS, which is 6.3X higher compared to the existing instruction-level model. SLoT-based reliability analysis of FUs under different datasets can achieve 0.7-4.8% average difference compared with GLS-based analysis, and execute more than 20X faster than GLS.