Abstract
The physical design process of large-scale designs is a time-consuming task,
often requiring hours to days to complete, with routing being the most critical
and complex step. As the the complexity of Integrated Circuits (ICs) increases,
there is an increased demand for accurate routing quality prediction. Accurate
congestion prediction aids in identifying design flaws early on, thereby
accelerating circuit design and conserving resources. 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, {\ours}, 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. {\ours} 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 {\ours} on the public available \texttt{ISPD2011} and
\texttt{DAC2012} benchmarks using the superblue circuit line.