In this paper, we propose a novel Dual-Prior Bayesian Model Fusion (DP-BMF) algorithm for performance modeling. Different from the previous BMF methods which use only one source of prior knowledge, DP-BMF takes advantage of multiple sources of prior knowledge to fully exploit the available information and, hence, further reduce the modeling cost. Based on a graphical model, an efficient Bayesian inference is developed to fuse two different prior models and combine the prior information with a small number of training samples to achieve high modeling accuracy. Several circuit examples demonstrate that the proposed method can achieve up to 1.83× cost reduction over the traditional one-prior BMF method without surrendering any accuracy.
['Qicheng Huang', 'Chenlei Fang', 'Fan Yang', 'Xuan Zeng', 'Dian Zhou', 'Xin Li']
Efficient performance modeling via Dual-Prior Bayesian Model Fusion for analog and mixed-signal circuits