Efficient multivariate moment estimation via Bayesian model fusion for analog and mixed-signal circuits
A critical-yet-challenging problem of analog/mixed-signal circuit validation in either pre-silicon or post-silicon stage is to estimate the parametric yield of the performances. In this paper, we propose a novel Bayesian model fusion method for efficient multivariate moment estimation of multiple correlated performance metrics by borrowing the prior knowledge from the early stage. The key idea is to model the multiple performance metrics as a jointly Gaussian distribution and encode the prior knowledge as a normal-Wishart distribution according to the theory of conjugate prior. The late-stage multivariate moments can be accurately estimated by Bayesian inference with very few late-stage samples. Several circuit examples demonstrate that the proposed method can achieve up to 16× cost reduction over the traditional method without surrendering any accuracy.
