Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148076
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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