论文标题
NET2：针对预位净长度估计的图形注意网络方法
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
论文作者
论文摘要
净长度是在标准数字设计流的各个阶段优化定时和功率的关键代理指标。但是，大部分净长度信息在细胞放置之前才可用，因此，在放置之前在设计阶段（例如逻辑合成）明确考虑在设计阶段中明确考虑净长度优化是一个重大挑战。这项工作通过提出具有自定义的图形注意网络方法（称为net2）来解决这一挑战，以估算细胞放置之前的单个净长度。与以前的几项在识别长网和长关键路径方面相比，其准确性的net2a的准确性要高15％。它的快速版本Net2F比放置的速度快1000倍以上，而在各种精确度指标方面仍然超过以前的作品和其他神经网络技术。
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.
