Towards efficient, on-demand and automated deep learning

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Yu, Jiahui

Contributors dc:contributor

• Huang, Thomas S.
• Liang, Zhi-Pei
• Hwu, Wen-Mei
• Lin, Zhe

• efficient, on-demand, automated, deep learning, automl

Statement dc:rights

• Copyright 2020 Jiahui Yu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/107845

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/107845