Pre-Placement Net Length and Timing Estimation by Customized Graph Neural Network

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. In addition, the absence of net length information makes accurate pre-placement timing estimation extremely difficult. Poor predictability on the timing not only affects timing optimizations but also hampers the accurate evaluation of synthesis solutions. This work addresses these challenges by a pre-placement prediction flow with estimators on both net length and timing. We propose 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× faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics. Based on net size estimations, we propose the first ML-based pre-placement timing estimator. Compared with the pre-placement timing report from commercial tools, it improves the correlation coefficient in arc delays by 0.08, and reduces the mean absolute error in slack, WNS, and TNS estimations by more than 50%.