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Title: | Collaborative deep learning inference in edge-cloud computing | Authors: | Lee, Martyn Eng Hui | Keywords: | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence | Issue Date: | 2021 | Publisher: | Nanyang Technological University | Source: | Lee, M. E. H. (2021). Collaborative deep learning inference in edge-cloud computing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148076 | Project: | SCSE20-0455 | Abstract: | With deep learning become more and more popular in machine learning literature, more research is being done to apply such tools to commercial and business use[25]. One of the more recent developments that comes to mind is collaborative inference. The topic of achieving better latency with collaborative inference has been well-studied[3,7], however those tests were concluded with state-of-the-art mobile edges that isn’t found in commercial devices. With the advent of more powerful mobile GPUs, it is a natural step to consider such latency and load-saving techniques for mobile devices that are on the market these days. The result of this came with qualified positive results with collaborative inference still being viable for commercial devices under certain conditions despite its clear GPU deficiency to its state-of-the-art counterparts. There exist certain strategies to consider when applying collaborative inference to commercial devices. | URI: | https://hdl.handle.net/10356/148076 | Schools: | School of Computer Science and Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
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File | Description | Size | Format | |
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FYPreport_MARTYNLEE_SCSE20-0455 (lib submission).pdf Restricted Access | 2.46 MB | Adobe PDF | View/Open |
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