Efficient performance modeling via Dual-Prior Bayesian Model Fusion for analog and mixed-signal circuits

Qicheng Huang, Chenlei Fang, Fan Yang, Xuan Zeng, Dian Zhou, Xin Li
2016 Proceedings of the 53rd Annual Design Automation Conference on - DAC '16  
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
more » ... all 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.
doi:10.1145/2897937.2898014 dblp:conf/dac/HuangFYZZL16 fatcat:vt7yj6d6nnh5zdlhceqo4xjcu4