A. Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, S. Dustdar, Thomas Hiessl, D. Kranzlmuller, Madhusanka Liyanage, Setareh Magshudi, Nitinder Mohan, J. Ott, Jan S. Rellermeyer, Stefan Schulte, H. Schulzrinne, Gürkan Solmaz, S. Tarkoma, B. Varghese, L. Wolf
{"title":"Roadmap for edge AI","authors":"A. Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, S. Dustdar, Thomas Hiessl, D. Kranzlmuller, Madhusanka Liyanage, Setareh Magshudi, Nitinder Mohan, J. Ott, Jan S. Rellermeyer, Stefan Schulte, H. Schulzrinne, Gürkan Solmaz, S. Tarkoma, B. Varghese, L. Wolf","doi":"10.1145/3523230.3523235","DOIUrl":null,"url":null,"abstract":"Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.","PeriodicalId":50646,"journal":{"name":"ACM Sigcomm Computer Communication Review","volume":"60 1","pages":"28 - 33"},"PeriodicalIF":2.2000,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Sigcomm Computer Communication Review","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3523230.3523235","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 25
Abstract
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.
期刊介绍：
Computer Communication Review (CCR) is an online publication of the ACM Special Interest Group on Data Communication (SIGCOMM) and publishes articles on topics within the SIG''s field of interest. Technical papers accepted to CCR typically report on practical advances or the practical applications of theoretical advances. CCR serves as a forum for interesting and novel ideas at an early stage in their development. The focus is on timely dissemination of new ideas that may help trigger additional investigations. While the innovation and timeliness are the major criteria for its acceptance, technical robustness and readability will also be considered in the review process. We particularly encourage papers with early evaluation or feasibility studies.
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