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Machine Learning Techniques for Building and Evaluation of Routability-driven Macro Placement

Routability evaluation in macro placement is a very important issue in the physical synthesis of VLSI design. In this paper, we propose to use two different machine learning methods simultaneously in the evaluation process, with the goal of exploring potential connections to improve the prediction accuracy of machine learning models. The method is integrated into the physical design flow and predict routability effectively during the placement stage to avoid infeasible design in the detailed routing stage. Experiment results show that our evaluation is accurate and effective.