LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces

TL;DR

LinEasyBO introduces a scalable Bayesian optimization method for analog circuit synthesis that reduces computational costs by focusing on one-dimensional subspaces, achieving significant acceleration over existing methods.

Contribution

The paper presents a novel one-dimensional subspace approach for Bayesian optimization in analog circuit synthesis, improving efficiency and scalability.

Findings

Accelerates optimization by up to 9x compared to LP-EI.

Achieves up to 38x speedup over REMBOpBO.

Effectively balances global and local search strategies.

Abstract

A large body of literature has proved that the Bayesian optimization framework is especially efficient and effective in analog circuit synthesis. However, most of the previous research works only focus on designing informative surrogate models or efficient acquisition functions. Even if searching for the global optimum over the acquisition function surface is itself a difficult task, it has been largely ignored. In this paper, we propose a fast and robust Bayesian optimization approach via one-dimensional subspaces for analog circuit synthesis. By solely focusing on optimizing one-dimension subspaces at each iteration, we greatly reduce the computational overhead of the Bayesian optimization framework while safely maximizing the acquisition function. By combining the benefits of different dimension selection strategies, we adaptively balancing between searching globally and locally. By leveraging the batch Bayesian optimization framework, we further accelerate the optimization procedure by making full use of the hardware resources. Experimental results quantitatively show that our proposed algorithm can accelerate the optimization procedure by up to 9x and 38x compared to LP-EI and REMBOpBO respectively when the batch size is 15.