Poona Bahrebar, Leon Denis, Maxim Bonnaerens, Kristof Coddens, J. Dambre, W. Favoreel, I. Khvastunov, A. Munteanu, Hung Nguyen-Duc, S. Schulte, D. Stroobandt, Ramses Valvekens, N. V. D. Broeck, Geert Verbruggen
{"title":"cREAtIve: reconfigurable embedded artificial intelligence","authors":"Poona Bahrebar, Leon Denis, Maxim Bonnaerens, Kristof Coddens, J. Dambre, W. Favoreel, I. Khvastunov, A. Munteanu, Hung Nguyen-Duc, S. Schulte, D. Stroobandt, Ramses Valvekens, N. V. D. Broeck, Geert Verbruggen","doi":"10.1145/3457388.3458857","DOIUrl":null,"url":null,"abstract":"cREAtIve targets the development of novel highly-adaptable embedded deep learning solutions for automotive and traffic monitoring applications, including position sensor processing, scene interpretation based on LiDAR, and object detection and classification in thermal images for traffic camera systems. These applications share the need for deep learning solutions tailored for deployment on embedded devices with limited resources and featuring high adaptability and robustness to changing environmental conditions. cREAtIve develops knowledge, tools and methods that enable hardware-efficient, adaptable, and robust deep learning.","PeriodicalId":136482,"journal":{"name":"Proceedings of the 18th ACM International Conference on Computing Frontiers","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3457388.3458857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
cREAtIve targets the development of novel highly-adaptable embedded deep learning solutions for automotive and traffic monitoring applications, including position sensor processing, scene interpretation based on LiDAR, and object detection and classification in thermal images for traffic camera systems. These applications share the need for deep learning solutions tailored for deployment on embedded devices with limited resources and featuring high adaptability and robustness to changing environmental conditions. cREAtIve develops knowledge, tools and methods that enable hardware-efficient, adaptable, and robust deep learning.