Paper Info
Title
An Efficient Multi-fidelity Bayesian Optimization Approach for Analog Circuit Synthesis
Abstract
This paper presents an efficient multi-fidelity Bayesian optimization approach for analog circuit synthesis. The proposed method can significantly reduce the overall computational cost by fusing the simple but potentially inaccurate low-fidelity model and a few accurate but expensive high-fidelity data. Gaussian Process (GP) models are employed to model the low- and high-fidelity black-box functions separately. The nonlinear map between the low-fidelity model and high-fidelity model is also modelled as a Gaussian process. A fusing GP model which combines the low- and high-fidelity models can thus be built. An acquisition function based on the fusing GP model is used to balance the exploitation and exploration. The fusing GP model is evolved gradually as new data points are selected sequentially by maximizing the acquisition function. Experimental results show that our proposed method reduces up to 65.5% of the simulation time compared with the state-of-the-art single-fidelity Bayesian optimization method, while exhibiting more stable performance and a more promising practical prospect.
Year
DOI
Venue
2019
10.1145/3316781.3317765
Proceedings of the 56th Annual Design Automation Conference 2019
Keywords
Field
DocType
Analog circuit synthesis, Gaussian process, Multi-fidelity Bayesian optimization
Data point,Fidelity,Nonlinear system,Computer science,Bayesian optimization,Algorithm,Real-time computing,Gaussian process
Conference
ISSN
ISBN
Citations 
The 56th Annual Design Automation Conference 2019
978-1-4503-6725-7
3
PageRank 
References 
Authors
0.39
10
7
Name
Order
Citations
PageRank
Shuhan Zhang1106.28
Wenlong Lyu2152.60
Fan Yang310122.74
Changhao Yan4276.64
Dian Zhou526056.14
Xuan Zeng640875.96
Xiang-Dong Hu733.09