Floorplan is an important process whose quality determines the timing closure in integrated circuit (IC) physical design. And generating a floorplan with satisfying timing result is time-consuming because much time is spent on the generation-evaluation iteration. Applying machine learning to the floorplan stage is a potential method to accelerate the floorplan iteration. However, there exist two challenges which are selecting proper features and achieving a satisfying model accuracy. In this paper, we propose a machine learning framework for floorplan acceleration with feature selection and model stacking to cope with the challenges, targeting to reduce time and effort in integrated circuit physical design. Specifically, the proposed framework supports predicting post-route slack of static random-access memory (SRAM) in the early floorplan stage. Firstly, we introduce a feature selection method to rank and select important features. Considering both feature importance and model accuracy, we reduce the number of features from 27 to 15 (44% reduction), which can simplify the dataset and help educate novice designers. Then, we build a stacking model by combining different kinds of models to improve accuracy. In 28 nm technology, we achieve the mean absolute error of slacks less than 23.03 ps and effectively accelerate the floorplan process by reducing evaluation time from 8 hours to less than 60 seconds. Based on our proposed framework, we can do design space exploration for thousands of locations of SRAM instances in few seconds, much more quickly than the traditional approach. In practical application, we improve the slacks of SRAMs more than 75.5 ps (177% improvement) on average than the initial design.

Key words: physical design; machine learning; feature selection; design space exploration;