Towards Accurate Machine-learning-assisted Aging Prediction with Probabilistic Strategy

Aging delay prediction is one of the key tasks for modern circuit design during reliability assessment. In traditional methods, static timing analysis is utilized to estimate accurate and effective guard bands. However, it requires intensive Monte-Carlo simulations which could bring high computational overheads. In modern IC design, deployment of neural networks assists to shorten time consumption and enhance prediction efficiency. Due to the black-box training procedure, the prediction results usually suffer from the uncertainty in networks and prediction accuracy could be unsatisfying in some risk-sensitive applications. In this work, we proposed a simple method to improve the aging delay predicted by machine-learning-based method. By implementing uncertainty estimates based on hybrid graphic neural distribution with Monte Carlo network and Dropout, prediction accuracy can be enhanced without further training while enabling the network to maintain high efficiency. Then, statistical analysis is developed based on predictive distribution and an innovative scheme for guard bands design is provided. Experiment results demonstrate that our method shows higher accuracy compared with benchmark networks with uncertainty unconsidered.