An Optimization-aware Pre-Routing Timing Prediction Framework Based on Heterogeneous Graph Learning

Abstract

Accurate and efficient pre-routing timing estimation is particularly crucial in timing-driven placement, as design iterations caused by timing divergence are time-consuming. However, existing machine learning prediction models overlook the impact of timing optimization techniques during routing stage, such as adjusting gate sizes or swapping threshold voltage types to fix routing-induced timing violations. In this work, an optimization-aware pre-routing timing prediction framework based on heterogeneous graph learning is proposed to calibrate the timing changes introduced by wire parasitic and optimization techniques. The path embedding generated by the proposed framework fuses learned local information from graph neural network and global information from transformer network to perform accurate endpoint arrival time prediction. Experimental results demonstrate that the proposed framework achieves an average accuracy improvement of 0.10 in terms of R2 score on testing designs and brings average runtime acceleration of three orders of magnitude compared with the design flow.