Towards efficient, on-demand and automated deep learning
Yu, Jiahui
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https://hdl.handle.net/2142/107845
Description
Title
Towards efficient, on-demand and automated deep learning
Author(s)
Yu, Jiahui
Issue Date
2020-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Doctoral Committee Chair(s)
Huang, Thomas S.
Committee Member(s)
Liang, Zhi-Pei
Hwu, Wen-Mei
Lin, Zhe
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(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.
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