PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop
  Prediction
Yuxiang Zhao Zhuomin Chai Xun Jiang Yibo Lin Runsheng Wang Ru Huang
IR drop on the power delivery network (PDN) is closely related to PDN's
configuration and cell current consumption. As the integrated circuit (IC)
design is growing larger, dynamic IR drop simulation becomes computationally
unaffordable and machine learning based IR drop prediction has been explored as
a promising solution. Although CNN-based methods have been adapted to IR drop
prediction task in several works, the shortcomings of overlooking PDN
configuration is non-negligible. In this paper, we consider not only how to
properly represent cell-PDN relation, but also how to model IR drop following
its physical nature in the feature aggregation procedure. Thus, we propose a
novel graph structure, PDNGraph, to unify the representations of the PDN
structure and the fine-grained cell-PDN relation. We further propose a
dual-branch heterogeneous network, PDNNet, incorporating two parallel GNN-CNN
branches to favorably capture the above features during the learning process.
Several key designs are presented to make the dynamic IR drop prediction highly
effective and interpretable. We are the first work to apply graph structure to
deep-learning based dynamic IR drop prediction method. Experiments show that
PDNNet outperforms the state-of-the-art CNN-based methods by up to 39.3%
reduction in prediction error and achieves 545x speedup compared to the
commercial tool, which demonstrates the superiority of our method.