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: A bi-directional GNN model for energy prediction of neural networks in IoT ... predict and deploy energy-efficient convolutional neural networks ...
This paper presents a novel energy prediction model, NeurstrucEnergy. NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional ...
作 者：Chaopeng Guo Zhaojin Zhong Zexin Zhang Jie Song Chaopeng Guo;Zhaojin Zhong;Zexin Zhang;Jie Song. 作者机构：Software CampusNortheastern ...
NeurstrucEnergy: A bi-directional GNN model for energy prediction of neural networks in IoT ; Source: Digital Communications and Networks ; ISSN 2352-8648.
NeurstrucEnergy: A bi-directional GNN model for energy prediction of neural networks in IoT. Article. Sep 2022. Chaopeng Guo · Zhaojin Zhong · Zexin ...
Jul 26, 2024 · This paper reviews state-of-the-art DL models for IoT, identifies their limitations, and explores how neuro-symbolic methods can overcome them.
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Our method of edge sampling preserves the core spectral features of the graph without affecting its fundamental structure. Our suggested technique outperforms ...
NeurstrucEnergy: A Bi-Directional GNN Model for Energy Prediction of Neural Networks in IoT, Digit. Commun. Netw., in press. https://doi.org/10.1016/j.dcan ...
NeurstrucEnergy: A bi-directional GNN model for energy prediction of neural networks in IoT. Chaopeng Guo et al., Digital Communications and Networks, 2022.