  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.
