{"title":"基于混合量和节点修剪的物联网节能计算神经网络效率优化","authors":"M. Helal Uddin, S. Baidya","doi":"10.1145/3576842.3589175","DOIUrl":null,"url":null,"abstract":"The Deep Neural Networks (DNN) are computationally intensive in terms of processing, energy and memory which becomes a bottleneck to run these models on edge devices. This research study provides a technique for pruning the neural networks to enhance the performance of deep learning models in IoT devices. The proposed method combines magnitude-based pruning, which merges insignificant weights based on their magnitude, with node pruning, which eliminates insignificant nodes based on their contribution to the network. The hybrid pruning technique is designed to be energy-efficient, reducing the computational overhead of deep learning models while maintaining their accuracy. The experimental results demonstrate that the proposed method can achieve significant reductions in model size and energy consumption with minimal loss in accuracy. The technique has the potential to enable the deployment of deep learning models on resource constrained IoT devices.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Neural Network Efficiency with Hybrid Magnitude-Based and Node Pruning for Energy-efficient Computing in IoT\",\"authors\":\"M. Helal Uddin, S. Baidya\",\"doi\":\"10.1145/3576842.3589175\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Deep Neural Networks (DNN) are computationally intensive in terms of processing, energy and memory which becomes a bottleneck to run these models on edge devices. This research study provides a technique for pruning the neural networks to enhance the performance of deep learning models in IoT devices. The proposed method combines magnitude-based pruning, which merges insignificant weights based on their magnitude, with node pruning, which eliminates insignificant nodes based on their contribution to the network. The hybrid pruning technique is designed to be energy-efficient, reducing the computational overhead of deep learning models while maintaining their accuracy. The experimental results demonstrate that the proposed method can achieve significant reductions in model size and energy consumption with minimal loss in accuracy. The technique has the potential to enable the deployment of deep learning models on resource constrained IoT devices.\",\"PeriodicalId\":266438,\"journal\":{\"name\":\"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3576842.3589175\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3576842.3589175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Neural Network Efficiency with Hybrid Magnitude-Based and Node Pruning for Energy-efficient Computing in IoT
The Deep Neural Networks (DNN) are computationally intensive in terms of processing, energy and memory which becomes a bottleneck to run these models on edge devices. This research study provides a technique for pruning the neural networks to enhance the performance of deep learning models in IoT devices. The proposed method combines magnitude-based pruning, which merges insignificant weights based on their magnitude, with node pruning, which eliminates insignificant nodes based on their contribution to the network. The hybrid pruning technique is designed to be energy-efficient, reducing the computational overhead of deep learning models while maintaining their accuracy. The experimental results demonstrate that the proposed method can achieve significant reductions in model size and energy consumption with minimal loss in accuracy. The technique has the potential to enable the deployment of deep learning models on resource constrained IoT devices.