Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This work addresses this challenge by proposing a graph attention network method with customization, called Net2, to estimate individual net length before cell placement. Its accuracy-oriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than 1000 times faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.

中文翻译：

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Net2：为预放置净长估计定制的图注意网络方法

净长是用于优化标准数字设计流程各个阶段的时序和功耗的关键代理指标。但是，大量净长度信息在单元放置之前不可用，因此在放置之前的设计阶段（例如逻辑综合）中明确考虑净长度优化是一项重大挑战。这项工作通过提出一种称为Net2的自定义图形关注网络方法来估计此问题，以估计单元放置之前的单个网络长度。其面向精度的版本Net2a在识别长网和长关键路径方面比以前的几项工作提高了约15％的精度。它的快速版本Net2f比放置速度快1000倍以上，但就各种精度指标而言，它仍然优于以前的著作和其他神经网络技术。