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

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 informati...

Full description

Saved in:
Bibliographic Details
Published in2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors Qicheng Huang, Chenlei Fang, Fan Yang, Xuan Zeng, Dian Zhou, Xin Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.06.2016
Subjects
Bayes methods
Computational modeling
Data models
Fitting
Graphical models
Integrated circuit modeling
Performance evaluation
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.1145/2897937.2898014