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Author Kukreja, Navjot ♦ Shilova, Alena ♦ Beaumont, Olivier ♦ Hückelheim, Jan ♦ Ferrier, Nicola ♦ Hovland, Paul ♦ Gorman, Gerard
Source Hyper Articles en Ligne (HAL)
Content type Text
File Format PDF
Language English
Subject Keyword info ♦ Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC] ♦ Computer Science [cs]/Machine Learning [cs.LG]
Abstract Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distribute the computing power and try to do as much processing as possible, close to the source of the data. There are various reasons this model is being adopted quickly, including privacy, and reduced power and bandwidth requirements on the Edge nodes. While it is common to see inference being done on Edge nodes today, it is much less common to do training on the Edge. The reasons for this range from computational limitations, to it not being advantageous in reducing communications between the Edge nodes. In this paper, we explore some scenarios where it is advantageous to do training on the Edge, as well as the use of checkpointing strategies to save memory.
Educational Use Research
Learning Resource Type Proceeding