{"title":"ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search","authors":"Behnam Zeinali;Di Zhuang;J. Morris Chang","doi":"10.1109/TETCI.2024.3485677","DOIUrl":null,"url":null,"abstract":"Deep neural networks have demonstrated superior performance in various computer vision tasks compared to traditional machine learning algorithms. However, deploying these models on resource-constrained mobile and IoT devices poses computational challenges. Many devices resort to cloud computing, where complex deep learning models analyze data on servers. This approach increases communication costs and hampers system efficiency in the absence of a network connection. In this paper, we introduce a novel framework for deploying deep neural networks on IoT devices. This framework leverages both cloud and on-device models by extracting meta-information from each sample's classification result. It assesses the classification's performance to determine whether sending the sample to the server is necessary. Extensive experiments on CIFAR10 and CINIC10 datasets reveal that only 45% of CIFAR10 and 60% of CINIC10 test data need to be transmitted to the server using this technique. The overall accuracy of the framework is 94% and 89%, respectively, enhancing the accuracy of both client and server models.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"961-971"},"PeriodicalIF":5.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10819965/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Deep neural networks have demonstrated superior performance in various computer vision tasks compared to traditional machine learning algorithms. However, deploying these models on resource-constrained mobile and IoT devices poses computational challenges. Many devices resort to cloud computing, where complex deep learning models analyze data on servers. This approach increases communication costs and hampers system efficiency in the absence of a network connection. In this paper, we introduce a novel framework for deploying deep neural networks on IoT devices. This framework leverages both cloud and on-device models by extracting meta-information from each sample's classification result. It assesses the classification's performance to determine whether sending the sample to the server is necessary. Extensive experiments on CIFAR10 and CINIC10 datasets reveal that only 45% of CIFAR10 and 60% of CINIC10 test data need to be transmitted to the server using this technique. The overall accuracy of the framework is 94% and 89%, respectively, enhancing the accuracy of both client and server models.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.