Learn from error! ML-based model error estimation for design verification without false-positives

Behavioral models are important tools for electronic system-level design and verification due to their simplicity and fast computing time. However, this simplicity is a double-edged sword as it introduces model errors that are hard to keep track of. In this paper, we present an approach to estimate the model error of behavioral models by utilizing an artificial neural network. This can be integrated into system-level simulations and provides online, reliable information on the validity of the model. Knowing the model error allows to evaluate trade-offs by tolerating errors where possible and ensuring precision where needed. Considering computing time, the proposed approach is low cost and aides the designer to balance speed and precision in the behavioral modeling of complex circuits. We showcase the methodology with a simple demonstration.