# An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis

arXiv: https://arxiv.org/abs/2106.14683 · HTML: https://mlsanity.com/paper/2106.14683/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [AutoML in Heavily Constrained Applications](https://mlsanity.com/paper/2306.16913/) (2023)
- [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)
- [Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization](https://mlsanity.com/paper/2603.13501/) (2026)
- [Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation](https://mlsanity.com/paper/2607.14165/) (2026)

## Abstract (arXiv)

In this paper, we propose EasyBO, an Efficient ASYnchronous Batch Bayesian Optimization approach for analog circuit synthesis. In this proposed approach, instead of waiting for the slowest simulations in the batch to finish, we accelerate the optimization procedure by asynchronously issuing the next query points whenever there is an idle worker. We introduce a new acquisition function that can better explore the design space for asynchronous batch Bayesian optimization. A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points. And a penalization scheme is proposed to further avoid redundant queries during the asynchronous batch optimization. The efficiency of optimization can thus be further improved. Compared with the state-of-the-art batch Bayesian optimization algorithm, EasyBO achieves up to 7.35 times speed-up without sacrificing the optimization results.