Bayesian model fusion: large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data

Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF)...

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Bibliographic Details
Main Authors: Wang, Fa, Zhang, Wangyang, Sun, Shupeng, Li, Xin, Gu, Chenjie
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Computational modeling
Computing methodologies > Modeling and simulation > Model development and analysis
Data models
Hardware > Electronic design automation > Physical design (EDA)
Hardware > Hardware validation
Integrated circuit modeling
Mathematical model
Numerical models
Semiconductor device modeling
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Summary:Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF). Our key idea is to borrow the simulation data generated from an early stage (e.g., schematic level) to facilitate efficient high-dimensional performance modeling at a late stage (e.g., post layout) with low computational cost. Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Several circuit examples designed in a commercial 32nm CMOS process demonstrate that BMF achieves up to 9x runtime speedup over the traditional modeling technique without surrendering any accuracy.
ISSN:0738-100X
DOI:10.1145/2463209.2488812