{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/107845"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/107845","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards efficient, on-demand and automated deep learning","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yu, Jiahui"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Thomas S.","Liang, Zhi-Pei","Hwu, Wen-Mei","Lin, Zhe"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:53:55Z","date_published":"2020-08-26T21:53:55Z","updated_at":"2026-07-22T22:24:47Z","subjects":["efficient, on-demand, automated, deep learning, automl"],"languages":["en"],"rights":["Copyright 2020 Jiahui Yu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/107845","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S.","Liang, Zhi-Pei","Hwu, Wen-Mei","Lin, Zhe"]},{"key":"dc:creator","label":"Author","values":["Yu, Jiahui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:53:55Z","2020-01-21","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["efficient, on-demand, automated, deep learning, automl"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Jiahui Yu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/107845"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Jiahui Yu, accepted the attached license on 2020-01-17 at 15:58.","The student, Jiahui Yu, submitted this Dissertation for approval on 2020-01-17 at 16:04.","This Dissertation was approved for publication on 2020-01-21 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14849 on 2020-08-25 at 17:03:08","Made available in DSpace on 2020-08-26T21:53:55Z (GMT). No. of bitstreams: 3 YU-DISSERTATION-2020.pdf: 2498738 bytes, checksum: d6fc5cf2e3f6cfe94a0339c8e6a93444 (MD5) LICENSE.txt: 4206 bytes, checksum: 9724ae490373d9c91b7c81ca6e090b30 (MD5) PROQUEST_LICENSE.txt: 4552 bytes, checksum: f244f3fdd68510197b4e67e15280cec4 (MD5) Previous issue date: 2020-01-21"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards efficient, on-demand and automated deep learning"]}]}],"canonical_facts":{"dc:contributor":["Huang, Thomas S.","Liang, Zhi-Pei","Hwu, Wen-Mei","Lin, Zhe"],"dc:creator":["Yu, Jiahui"],"dc:date":["2020-08-26T21:53:55Z","2020-01-21","2020-05"],"dc:description":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Jiahui Yu, accepted the attached license on 2020-01-17 at 15:58.","The student, Jiahui Yu, submitted this Dissertation for approval on 2020-01-17 at 16:04.","This Dissertation was approved for publication on 2020-01-21 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14849 on 2020-08-25 at 17:03:08","Made available in DSpace on 2020-08-26T21:53:55Z (GMT). No. of bitstreams: 3 YU-DISSERTATION-2020.pdf: 2498738 bytes, checksum: d6fc5cf2e3f6cfe94a0339c8e6a93444 (MD5) LICENSE.txt: 4206 bytes, checksum: 9724ae490373d9c91b7c81ca6e090b30 (MD5) PROQUEST_LICENSE.txt: 4552 bytes, checksum: f244f3fdd68510197b4e67e15280cec4 (MD5) Previous issue date: 2020-01-21"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/107845"],"dc:language":["en"],"dc:rights":["Copyright 2020 Jiahui Yu"],"dc:subject":["efficient, on-demand, automated, deep learning, automl"],"dc:title":["Towards efficient, on-demand and automated deep learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:47Z"}