Co-Learning Bayesian Model Fusion: Efficient performance modeling of analog and mixed-signal circuits using side information

Abstract

Efficient performance modeling of today's analog and mixed-signal (AMS) circuits is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Co-Learning Bayesian Model Fusion (CL-BMF). The key idea of CL-BMF is to take advantage of the additional information collected from simulation and/or measurement to reduce the performance modeling cost. Different from the traditional performance modeling approaches which focus on the prior information of model coefficients (i.e. the coefficient side information) only, CL-BMF takes advantage of another new form of prior knowledge: the performance side information. In particular, CL-BMF combines the coefficient side information, the performance side information and a small number of training samples through Bayesian inference based on a graphical model. Two circuit examples designed in a commercial 32nm SOI CMOS process demonstrate that CL-BMF achieves up to 5× speed-up over other state-of-the-art performance modeling techniques without surrendering any accuracy.