用神经网络融合电路图与网表特征，快速精准预测静态IR降压。
CFIRSTNET: Comprehensive Features for Static IR Drop Estimation with Neural Network
- 结合图像与网表特征的定制卷积网络提取电源网络信息。
- 在ICCAD 2023竞赛中达到最佳预测精度，误差低于基准方案。
- 适合芯片设计中的电源完整性分析与快速验证场景。
由于现代电子产品的可靠性和性能问题日益突出，IR降压估计已成为关键指标。传统方法依赖冗长的迭代与仿真流程，如何实现快速且准确的估计成为迫切需求。本文借助现代AI加速技术，提出一种综合方案，在神经网络框架中融合基于图像与网表的特征，高效实现现代集成电路设计中的高精度静态IR降压预测。我们设计了一种定制化的卷积神经网络（CNN）用于提取电源分布网络（PDN）特征并进行估计。在开源数据集上训练与评估，实验结果表明，本方法在ICCAD CAD竞赛2023的IR降压估计任务中取得了最优表现，验证了该设计课题的有效性。
原文摘要 · Abstract (English)
IR drop estimation is now considered a first-order metric due to the concern about reliability and performance in modern electronic products. Since traditional solution involves lengthy iteration and simulation flow, how to achieve fast yet accurate estimation has become an essential demand. In this work, with the help of modern AI acceleration techniques, we propose a comprehensive solution to combine both the advantages of image-based and netlist-based features in neural network framework and obtain high-quality IR drop prediction very effectively in modern designs. A customized convolutional neural network (CNN) is developed to extract PDN features and make static IR drop estimations. Trained and evaluated with the open-source dataset, experiment results show that we have obtained the best quality in the benchmark on the problem of IR drop estimation in ICCAD CAD Contest 2023, proving the effectiveness of this important design topic.
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