FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
This work addresses the problem of time-consuming and manual chip design flow parameter tuning for chip designers, offering an automated solution.
This paper introduces a machine learning-based methodology for automatic design flow parameter tuning in chip design, aiming to optimize design quality with limited trials. The method achieves a 25% improvement in design quality or a 37% reduction in sampling cost compared to random forest methods, and reduces area by 1.83% and 1.43% on industrial designs by sampling less than 0.02% of possible parameter sets.
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.