  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.
