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.

In this paper, we propose a novel system architecture called multi-layer environmental affordance map for social and service companion robots. Based on this architecture, robots can organize the perception and inference information efficiently and generate social friendly navigation strategies. In other words, robots are able to strengthen their perception and inference abilities to interact with domestic environment and users under our efficient framework. The main feature of this architecture is that the relations between layers can be viewed as affordances to improve the accuracy and the robustness of the detection and inference. The results show that our architecture achieves robust indoor localization, scene localization, human event detection and socially friendly navigation in real time under limited computational resource.