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%.

中文翻译：

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PROS 2.0：使用深度学习进行布线优化和布线线长估计的插件


最近，如何在 EDA 流程中利用机器学习 (ML) 技术获得的先验知识这一主题已得到广泛研究。在本文中，我们研究了这个主题，并提出了一个名为 PROS 的实用插件，用于可布线性优化和布线线长估计，该插件可应用于最先进的商业 EDA 工具或学术 EDA 流程，且运行时开销可以忽略不计。 PROS由三部分组成：1）一个有效的基于全卷积网络（FCN）的预测器，仅利用布局结果的数据来预测全局路由（GR）拥塞； 2）参数优化器，可以根据预测结果合理调整GR成本参数，为详细路由（DR）生成更好的GR解决方案； 3）基于卷积神经网络（CNN）的线长估计器，可以使用预测的 GR 拥塞在布局阶段报告准确的布线线长。实验表明，在先进技术节点的工业基准套件上，PROS可以实现高精度的GR拥塞预测，并平均显着减少11.65%的设计规则检查（DRC）违规，并且在DAC-2012基准套件上，PROS可以线长估计的错误率非常低（1.82%），大大优于 FLUTE（21.52%）19.70%。