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

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