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Correlated Bayesian Model Fusion: Efficient performance modeling of large-scale tunable analog/RF integrated circuits

Publication ,  Conference
Wang, F; Li, X
Published in: Proceedings - Design Automation Conference
June 5, 2016

Tunable circuit has emerged as a promising methodology to address the grand challenge posed by process variations. Efficient high-dimensional performance modeling of tunable analog/RF circuits is an important yet challenging task. In this paper, we propose a novel performance modeling approach for tunable circuits, referred to as Correlated Bayesian Model Fusion (C-BMF). The key idea is to encode the correlation information for both model template and coefficient magnitude among different knob configurations by using a unified prior distribution. The prior distribution is then combined with a few simulation samples via Bayesian inference to efficiently determine the unknown model coefficients. Two circuit examples designed in a commercial 32nm SOI CMOS process demonstrate that C-BMF achieves more than 2× cost reduction over the traditional state-of-the-art modeling technique without surrendering any accuracy.

Duke Scholars

Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

ISBN

9781450342360

Publication Date

June 5, 2016

Volume

05-09-June-2016
 

Citation

APA
Chicago
ICMJE
MLA
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Wang, F., & Li, X. (2016). Correlated Bayesian Model Fusion: Efficient performance modeling of large-scale tunable analog/RF integrated circuits. In Proceedings - Design Automation Conference (Vol. 05-09-June-2016). https://doi.org/10.1145/2897937.2897999
Wang, F., and X. Li. “Correlated Bayesian Model Fusion: Efficient performance modeling of large-scale tunable analog/RF integrated circuits.” In Proceedings - Design Automation Conference, Vol. 05-09-June-2016, 2016. https://doi.org/10.1145/2897937.2897999.
Wang, F., and X. Li. “Correlated Bayesian Model Fusion: Efficient performance modeling of large-scale tunable analog/RF integrated circuits.” Proceedings - Design Automation Conference, vol. 05-09-June-2016, 2016. Scopus, doi:10.1145/2897937.2897999.

Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

ISBN

9781450342360

Publication Date

June 5, 2016

Volume

05-09-June-2016