Variational Label-Correlation Enhancement for Congestion Prediction

As the complexity of Integrated Circuits (ICs) rises, accurate routing and congestion prediction, crucial for identifying early design flaws, become essential to expedite circuit design and conserve resources in the lengthy physical design process. Despite the advancements in current congestion prediction methodologies, an essential aspect that has been largely overlooked is the spatial label-correlation between different grids in congestion prediction. The spatial label-correlation is a fundamental characteristic of circuit design, where the congestion status of a grid is not isolated but inherently influenced by the conditions of its neighboring grids. In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, VALCE, i.e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids. VALCE leverages variational inference techniques to estimate this weight, thereby enhancing the regression model’s performance by incorporating spatial dependencies. Experiment results validate the superior effectiveness of VALCE on the public available ISPD2011 and DAC2012 benchmarks using the superblue circuit line.