An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis

TL;DR

EasyBO is a novel asynchronous batch Bayesian optimization method for analog circuit synthesis that accelerates the process by issuing queries asynchronously, introducing new strategies for exploration, diversity, and redundancy reduction, achieving significant speed-ups.

Contribution

This paper introduces EasyBO, a new asynchronous batch Bayesian optimization approach with novel acquisition, exploration, and penalization strategies for faster analog circuit synthesis.

Findings

Achieves up to 7.35x speed-up over state-of-the-art methods.

Maintains optimization quality despite increased speed.

Effectively balances exploration and exploitation in asynchronous settings.

Abstract

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.