# An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble

arXiv: https://arxiv.org/abs/2106.15412 · HTML: https://mlsanity.com/paper/2106.15412/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [An Entropy Search Portfolio for Bayesian Optimization](https://mlsanity.com/paper/1406.4625/) (2014)
- [An Efficient Multi-fidelity Bayesian Optimization Approach for Analog Circuit Synthesis](https://mlsanity.com/paper/1912.00392/) (2019)
- [Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network](https://mlsanity.com/paper/1912.00402/) (2019)

## Built on by (most cited first, 13 of 13)

- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Batch Bayesian optimisation via density-ratio estimation with guarantees](https://mlsanity.com/paper/2209.10715/) (2022)
- [SCOOT: SLO-Oriented Performance Tuning for LLM Inference Engines](https://mlsanity.com/paper/2408.04323/) (2024)
- [Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights](https://mlsanity.com/paper/2501.11839/) (2025)
- [HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization](https://mlsanity.com/paper/2511.19669/) (2025)
- [A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction](https://mlsanity.com/paper/2506.18424/) (2025)
- [Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks](https://mlsanity.com/paper/2302.07260/) (2023)
- [KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology](https://mlsanity.com/paper/2404.14433/) (2024)
- [Exploiting Function-Family Structure in Analog Circuit Optimization](https://mlsanity.com/paper/2512.00712/) (2025)
- [GLOVA: Global and Local Variation-Aware Analog Circuit Design with Risk-Sensitive Reinforcement Learning](https://mlsanity.com/paper/2505.11208/) (2025)
- [Emerging ML-AI Techniques for Analog and RF EDA](https://mlsanity.com/paper/2506.00007/) (2025)
- [Unified Audio Intelligence Without Regressing on Text Intelligence](https://mlsanity.com/paper/2607.05196/) (2026)
- [Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices](https://mlsanity.com/paper/2507.19663/) (2025)

## Abstract (arXiv)

Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this paper, we propose an efficient parallelizable Bayesian optimization algorithm via Multi-objective ACquisition function Ensemble (MACE) to further accelerate the optimization procedure. By sampling query points from the Pareto front of the probability of improvement (PI), expected improvement (EI) and lower confidence bound (LCB), we combine the benefits of state-of-the-art acquisition functions to achieve a delicate tradeoff between exploration and exploitation for the unconstrained optimization problem. Based on this batch design, we further adjust the algorithm for the constrained optimization problem. By dividing the optimization procedure into two stages and first focusing on finding an initial feasible point, we manage to gain more information about the valid region and can better avoid sampling around the infeasible area. After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble. The experimental results quantitatively demonstrate that our proposed algorithm can reduce the overall simulation time by up to 74 times compared to differential evolution (DE) for the unconstrained optimization problem when the batch size is 15. For the constrained optimization problem, our proposed algorithm can speed up the optimization process by up to 15 times compared to the weighted expected improvement based Bayesian optimization (WEIBO) approach, when the batch size is 15.