Bayesian Active Learning for Uncertainty Quantification of High Speed Channel Signaling

Increasing data rates in server high-speed communication busses makes their performance more susceptible to uncertainties in manufacturing processes. As a result, it is essential to understand channel design limitations and performance under tolerances to ensure a robust system. Predicting channel performance under tolerances can become very straining in time and computational resources. To address this, we propose a new active learning based algorithm that starts with no training data to simultaneously derive an accurate predictive model while finding the worst case scenario to ensure channel compliance in reduced CPU time compared to conventional methods.