Net²: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

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 Net ² , to estimate individual net length before cell placement. Its accuracy-oriented version Net ^2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net ^2f is more than 1000× faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.