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Title:Towards efficient, on-demand and automated deep learning
Author(s):Yu, Jiahui
Director of Research:Huang, Thomas S.
Doctoral Committee Chair(s):Huang, Thomas S.
Doctoral Committee Member(s):Liang, Zhi-Pei; Hwu, Wen-Mei; Lin, Zhe
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):efficient, on-demand, automated, deep learning, automl
Abstract:In the past decade, deep learning has achieved great breakthroughs on tasks of computer vision, speech, language, control and many others. The advanced and dedicated computing chips, like Nvidia GPU and Google TPU, largely contributed and broadened this success. However, the requirement of large computing power impedes the deployment of deep learning methods in many real scenarios, where cost, time and energy efficiency are critical -- for example, self-driving cars, AR/VR kits, internet-of-things devices and mobile phones. This thesis presents a series of in-depth research towards efficient, on-demand and automated deep learning.
Issue Date:2020-01-21
Type:Thesis
URI:http://hdl.handle.net/2142/107845
Rights Information:Copyright 2020 Jiahui Yu
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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