FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

TL;DR

This paper presents FIST, a machine learning-based method using feature importance sampling and tree techniques for automatic design flow parameter tuning, significantly reducing manual effort and improving chip design quality.

Contribution

It introduces a novel clustering and sampling approach combined with a dynamic tree model to enhance tuning efficiency and knowledge reuse in chip design parameter optimization.

Findings

Achieves 25% better design quality or 37% lower sampling cost than previous methods.

Reduces area by up to 1.83% on industrial designs using minimal sampling.

Effectively leverages prior knowledge for improved parameter tuning.

Abstract

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-based automatic parameter tuning methodology that aims to find the best design quality with a limited number of trials. Instead of merely plugging in machine learning engines, we develop clustering and approximate sampling techniques for improving tuning efficiency. The feature extraction in this method can reuse knowledge from prior designs. Furthermore, we leverage a state-of-the-art XGBoost model and propose a novel dynamic tree technique to overcome overfitting. Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work. Our approach is further validated on two industrial designs. By sampling less than 0.02% of possible parameter sets, it reduces area by 1.83% and 1.43% compared to the best solutions hand-tuned by experienced designers.