GIRD: A Green IR-Drop Estimation Method

Abstract

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.