1. [Publications](/publications) 2. FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning # FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning ![](/sites/default/files/styles/wide/public/publications/FIST_capture.PNG?itok=sNBoTLeI) 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. ## Authors Zhiyao Xie (Duke) Guan-Qi Fang (NTUST) Yu-Hung Huang (NTUST) Mark Haoxing Ren (NVIDIA) [Yanqing Zhang](/person/yanqing-zhang) [Brucek Khailany](/person/brucek-khailany) Shao-Yun Fang (NTUST) Jiang Hu (Texas A&M) Yiran Chen (Duke) Erick Carvajal Barboza (Texas A&M) ## Publication Date Monday, January 13, 2020 ## Published in [ASP-DAC 2020](https://aspdac2020.github.io/aspdac20/welcome/index.html) ## Research Area [Circuits and VLSI Design](/research-area/circuits) [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) ## Uploaded Files [ASPDAC20\_FIST\_Final.pdf](https://research.nvidia.com/sites/default/files/publications/ASPDAC20_FIST_Final.pdf "Open file in new window")6.5 MB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .