Kailai Sun , Xinwei Wang , Xi Miao , Qianchuan Zhao
{"title":"人工智能边缘设备及轻量级 CNN 和 LLM 部署回顾","authors":"Kailai Sun , Xinwei Wang , Xi Miao , Qianchuan Zhao","doi":"10.1016/j.neucom.2024.128791","DOIUrl":null,"url":null,"abstract":"<div><div>Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, convolutional neural networks (CNN) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper investigates literature that deploys lightweight CNNs on AI edge devices in practice. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight CNNs, select suitable AI edge devices, and compress and deploy them in practice. Finally, future trends and opportunities are presented, including the deployment of large language models, trustworthy AI and robust deployment.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review of AI edge devices and lightweight CNN and LLM deployment\",\"authors\":\"Kailai Sun , Xinwei Wang , Xi Miao , Qianchuan Zhao\",\"doi\":\"10.1016/j.neucom.2024.128791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, convolutional neural networks (CNN) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper investigates literature that deploys lightweight CNNs on AI edge devices in practice. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight CNNs, select suitable AI edge devices, and compress and deploy them in practice. Finally, future trends and opportunities are presented, including the deployment of large language models, trustworthy AI and robust deployment.</div></div>\",\"PeriodicalId\":19268,\"journal\":{\"name\":\"Neurocomputing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurocomputing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925231224015625\",\"RegionNum\":2,\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224015625","RegionNum":2,"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}
A review of AI edge devices and lightweight CNN and LLM deployment
Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, convolutional neural networks (CNN) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper investigates literature that deploys lightweight CNNs on AI edge devices in practice. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight CNNs, select suitable AI edge devices, and compress and deploy them in practice. Finally, future trends and opportunities are presented, including the deployment of large language models, trustworthy AI and robust deployment.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.