{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/114026"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/114026","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Acceleration of deep learning applications for astrophysics on FPGAs","abstract":"The student, Mengshen Yun, accepted the attached license on 2021-12-09 at 10:48.","abstract_html":"The student, Mengshen Yun, accepted the attached license on 2021-12-09 at 10:48.","abstract_has_math":false,"creators":["Yun, Mengshen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kindratenko, Volodymyr"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:47:47Z","date_published":"2022-04-29T21:47:47Z","updated_at":"2026-07-22T22:24:54Z","subjects":["Engineering"],"languages":["en","eng"],"rights":["Copyright 2021 Mengshen Yun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/114026","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kindratenko, Volodymyr"]},{"key":"dc:creator","label":"Author","values":["Yun, Mengshen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:47:47Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Mengshen Yun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/114026"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Mengshen Yun, accepted the attached license on 2021-12-09 at 10:48.","The student, Mengshen Yun, submitted this Thesis for approval on 2021-12-09 at 10:56.","This Thesis was approved for publication on 2021-12-09 at 15:46.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","DSpace SAF Submission Ingestion Package generated from Vireo submission #17431 on 2022-04-06 at 17:18:05","Made available in DSpace on 2022-04-29T21:47:47Z (GMT). No. of bitstreams: 2 YUN-THESIS-2021.pdf: 846611 bytes, checksum: 2baa7bf91d3f6672fabdcadaece40f4f (MD5) LICENSE.txt: 4210 bytes, checksum: bf8fc75552269c51b9752cb2b24302df (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123391 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","Deep learning algorithms have been widely used in the past decade due to their effectiveness and robustness in information processing in various scientific domains. With the evolvement of large-scale datasets and deep learning applications used for gravitational wave astrophysics and large-scale electromagnetic surveys, an accessible and efficient framework is essential to accelerate domain-specific applications on acceleration platforms. Although CPUs and GPUs are common platforms for training and inferencing deep learning applications given their ease-of-use and support for popular deep learning frameworks, field-programmable gate array (FPGA)-based accelerators have rapidly gained their popularity because of their capacity to deliver superior performance for real-time applications with relatively low power consumption. In this thesis, we investigate the process of porting deep learning algorithms developed for Multi-Messenger Astrophysics and particle physics on FPGA accelerators and propose a cyberinfrastructure that enables the use of FPGAs to accelerate algorithms inference. Also, we explore and evaluate three main development stacks, hls4ml, Vitis AI, and TVM, used for deploying deep neural network (DNN) models on cloud FPGA-based hardware accelerators."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Acceleration of deep learning applications for astrophysics on FPGAs"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Yun, Mengshen"],"dc:date":["2022-04-29T21:47:47Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"],"dc:description":["The student, Mengshen Yun, accepted the attached license on 2021-12-09 at 10:48.","The student, Mengshen Yun, submitted this Thesis for approval on 2021-12-09 at 10:56.","This Thesis was approved for publication on 2021-12-09 at 15:46.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","DSpace SAF Submission Ingestion Package generated from Vireo submission #17431 on 2022-04-06 at 17:18:05","Made available in DSpace on 2022-04-29T21:47:47Z (GMT). No. of bitstreams: 2 YUN-THESIS-2021.pdf: 846611 bytes, checksum: 2baa7bf91d3f6672fabdcadaece40f4f (MD5) LICENSE.txt: 4210 bytes, checksum: bf8fc75552269c51b9752cb2b24302df (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123391 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","Deep learning algorithms have been widely used in the past decade due to their effectiveness and robustness in information processing in various scientific domains. With the evolvement of large-scale datasets and deep learning applications used for gravitational wave astrophysics and large-scale electromagnetic surveys, an accessible and efficient framework is essential to accelerate domain-specific applications on acceleration platforms. Although CPUs and GPUs are common platforms for training and inferencing deep learning applications given their ease-of-use and support for popular deep learning frameworks, field-programmable gate array (FPGA)-based accelerators have rapidly gained their popularity because of their capacity to deliver superior performance for real-time applications with relatively low power consumption. In this thesis, we investigate the process of porting deep learning algorithms developed for Multi-Messenger Astrophysics and particle physics on FPGA accelerators and propose a cyberinfrastructure that enables the use of FPGAs to accelerate algorithms inference. Also, we explore and evaluate three main development stacks, hls4ml, Vitis AI, and TVM, used for deploying deep neural network (DNN) models on cloud FPGA-based hardware accelerators."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/114026"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Mengshen Yun"],"dc:subject":["Engineering"],"dc:title":["Acceleration of deep learning applications for astrophysics on FPGAs"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}