An energy-efficient high-performance static IR-drop estimation method based on green learning called Green IR Drop (GIRD) is proposed in this work. GIRD processes the IC design input in three steps. First, the input netlist data are converted to multichannel maps. Their joint spatial–spectral representations are determined with PixelHop. Next, discriminant features are selected using the relevant feature test (RFT). Finally, the selected features are fed to the eXtreme Gradient Boosting trees regressor. Both PixelHop and RFT are green learning tools. GIRD yields a low carbon footprint due to its smaller model sizes and lower computational complexity. Besides, its performance scales well with small training datasets. Experiments on synthetic and real circuits are given to demonstrate the superior performance of GIRD. The model size and the complexity, measured by the floating point operations (FLOPs) of GIRD, are only$10^{-3}$and$10^{-2}$of deep-learning methods, respectively.

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.