NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT
作者机构：Software CampusNortheastern UniversityShenyang110000LiaoningChina
出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)
年 卷 期：2024年第10卷第2期
页 面：439-449页
核心收录：
学科分类：0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：supported by the Natural Science Foundation of Liaoning Province(2020-BS-054) the Fundamental Research Funds for the Central Universities(N2017005) the National Natural Science Foundation of China(62162050)
主 题：Internet of things Neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention
摘 要：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 *** accurate energy prediction approach is critical to provide measurement and lead optimization ***,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 *** paper presents a novel energy prediction model,*** 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 *** has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer s parents and *** results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture *** code is available at https://***/NEUSoftGreenAI/***.
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