PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction

• URL: http://arxiv.org/abs/2403.18569v1
• Date: Wed, 27 Mar 2024 13:50:13 GMT
• Title: PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
• Authors: Yuxiang Zhao, Zhuomin Chai, Xun Jiang, Yibo Lin, Runsheng Wang, Ru Huang,
• Abstract summary: IR drop on the power delivery network (PDN) is closely related to PDN's configuration and cell current consumption. We propose a novel graph structure, PDNGraph, to unify the representations of the PDN structure and the cell-PDN relation. We are the first work to apply graph structure to deep-learning based dynamic IR drop prediction method.
• Score: 5.511978576494924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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