PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep Learning

Abstract

Recently, the topic of how to utilize prior knowledge obtained by machine-learning (ML) techniques during the EDA flow has been widely studied. In this article, we study this topic and propose a practical plug-in named PROS for both routability optimization and routed wirelength estimation which can be applied in the state-of-the-art commercial EDA tool or an academic EDA flow with negligible runtime overhead. PROS consists of three parts: 1) an effective fully convolutional network (FCN)-based predictor that only utilizes the data from placement result to forecast global routing (GR) congestion; 2) a parameter optimizer that can reasonably adjust GR cost parameters based on the prediction result to generate a better GR solution for detailed routing (DR); and 3) a convolutional neural network (CNN)-based wirelength estimator which can report accurate routed wirelength at the placement stage by using the predicted GR congestion. Experiments show that on the industrial benchmark suite in the advanced technology node, PROS can achieve high accuracy of GR congestion prediction and significantly reduce design rule checking (DRC) violations by 11.65% on average, and on the DAC-2012 benchmark suite, PROS can achieve a very low error rate (1.82%) for wirelength estimation which greatly outperforms that of FLUTE (21.52%) by 19.70%.