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

Conference Paper

© 2016 ACM. 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.

Full Text

Duke Authors

Cited Authors

  • Huang, Q; Fang, C; Yang, F; Zeng, X; Zhou, D; Li, X

Published Date

  • June 5, 2016

Published In

Volume / Issue

  • 05-09-June-2016 /

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781450342360

Digital Object Identifier (DOI)

  • 10.1145/2897937.2898014

Citation Source

  • Scopus