Detailed routing is one of the most challenging aspects of the physical design process. Many of the violations that occur during the detailed routing stage stem from the placement of the cells. In this paper, we propose a deep learning framework to identify short violations that can occur during detailed routing from a placed netlist. One of the advantages of our technique is that by using the proposed deep learning-based predictor, global routing is no longer required as frequently and hence the total runtime for place and route can be significantly reduced. In this paper, we discuss the proposed framework and the methodology for analyzing the extracted features. The experimental results show that the average sensitivity, specificity, and accuracy of Eh?Predictor is above 90%. In addition, we show that Eh?Predictor is up to 14 times faster than NCTUgr for smaller designs and up to 96 times faster for larger designs.

中文翻译：

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Eh?Predictor：一种从放置的网表中识别详细路由短路违规的深度学习框架

详细布线是物理设计过程中最具挑战性的方面之一。在详细布线阶段发生的许多违规源于单元的放置。在本文中，我们提出了一个深度学习框架来识别在从放置的网表进行详细布线期间可能发生的短时间违规。我们技术的优势之一是，通过使用所提出的基于深度学习的预测器，不再需要频繁地进行全局布线，因此可以显着减少布局和布线的总运行时间。在本文中，我们讨论了所提出的框架和分析提取特征的方法。实验结果表明，Eh?Predictor的平均灵敏度、特异性和准确度均在90%以上。此外，