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A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields. An accurate energy prediction approach is critical to provide measurement and lead optimization direction. However, the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset. This paper presents a novel energy prediction model, NeurstrucEnergy. NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction. NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from …