# An Efficient Multi-fidelity Bayesian Optimization Approach for Analog Circuit Synthesis

arXiv: https://arxiv.org/abs/1912.00392 · HTML: https://mlsanity.com/paper/1912.00392/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble](https://mlsanity.com/paper/2106.15412/) (2021)
- [LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation](https://mlsanity.com/paper/2406.05250/) (2024)
- [Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning](https://mlsanity.com/paper/2403.08152/) (2024)
- [High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling](https://mlsanity.com/paper/2103.17236/) (2021)
- [On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization](https://mlsanity.com/paper/2309.02771/) (2023)
- [Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures](https://mlsanity.com/paper/2507.22079/) (2025)
- [BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver](https://mlsanity.com/paper/2108.00257/) (2021)
- [LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces](https://mlsanity.com/paper/2109.00617/) (2021)
- [Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices](https://mlsanity.com/paper/2507.19663/) (2025)

## Abstract (arXiv)

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.