RankTuner: When Design Tool Parameter Tuning Meets Preference Bayesian Optimization

Abstract

Electronic design automation (EDA) tools are critical in the very large scale integration (VLSI) flow. To address the challenges posed by the extensive search space and intricate feature interactions, statistical and machine learning methods have been employed. These methods aim to model tool parameters and treat the tuning process as a regression task. However, these regression-based methods suffer from inaccurate estimations owing to limited training samples. To address this issue, we propose a ranking-based tool parameter tuning framework, called RankTuner, which directly learns the dominant relationship between parameters. RankTuner utilizes a pairwise Gaussian process (GP) to estimate the probability and uncertainty of the dominance relationship. Our approach also integrates a Duel–Thompson sampling method to balance exploration and exploitation in parameter selections. A dimensionality reduction scheme with random embedding and trust region (TR) techniques is incorporated to enable parallel searches. Experimental results demonstrate the superiority of RankTuner compared to the cutting-edge tool parameter tuning methods.