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dc.contributor.authorMarchisio, Albertoen
dc.contributor.authorHanif, Muhammad Abdullahen
dc.contributor.authorKhalid, Faiqen
dc.contributor.authorPlastiras, Georgeen
dc.contributor.authorKyrkou, Christosen
dc.contributor.authorTheocharides, Theocharisen
dc.contributor.authorShafique, Muhammaden
dc.creatorMarchisio, Albertoen
dc.creatorHanif, Muhammad Abdullahen
dc.creatorKhalid, Faiqen
dc.creatorPlastiras, Georgeen
dc.creatorKyrkou, Christosen
dc.creatorTheocharides, Theocharisen
dc.creatorShafique, Muhammaden
dc.description.abstractIn the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.en
dc.source2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)en
dc.titleDeep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challengesen
dc.description.endingpage559Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidKyrkou, Christos [0000-0002-7926-7642]
dc.contributor.orcidTheocharides, Theocharis [0000-0001-7222-9152]

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