{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108153"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108153","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Boosting static timing analysis with programming and algorithmic approaches","abstract":"The increasing complexity in digital design has spurred demand for faster design closure. As a primary timing measurement tool frequently used in design stage and optimization stage, static timing analysis has become one of the major performance bottlenecks in digital design. We study a novel parallel programming model and algorithm to boost timing analysis. As multi-core systems have become common in modern electronics, how to fit timing analysis into the multithreading environment is a trending research topic. We explore this direction with a new task-based multithreading framework and demonstrate its superior efficiency over existing tools. Critical path generation is a major objective timing analysis. Optimization tools always need to report on critical paths under several path constraints. We propose a general path search algorithm which can fulfill all practical path constraints and outperforms an industrial timing analysis tool. Combining the tasks proposed above, we aim to improve the efficiency of static timing analysis with both a new programming framework and new algorithm.","abstract_html":"The increasing complexity in digital design has spurred demand for faster design closure. As a primary timing measurement tool frequently used in design stage and optimization stage, static timing analysis has become one of the major performance bottlenecks in digital design. We study a novel parallel programming model and algorithm to boost timing analysis. As multi-core systems have become common in modern electronics, how to fit timing analysis into the multithreading environment is a trending research topic. We explore this direction with a new task-based multithreading framework and demonstrate its superior efficiency over existing tools. Critical path generation is a major objective timing analysis. Optimization tools always need to report on critical paths under several path constraints. We propose a general path search algorithm which can fulfill all practical path constraints and outperforms an industrial timing analysis tool. Combining the tasks proposed above, we aim to improve the efficiency of static timing analysis with both a new programming framework and new algorithm.","abstract_has_math":false,"creators":["Guo, Guannan"],"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":["Wong, Martin D.F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:37Z","date_published":"2020-08-26T23:58:37Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Static Timing Analysis","Parallel Programming"],"languages":["en"],"rights":["Copyright 2020 Guannan Guo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108153","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wong, Martin D.F."]},{"key":"dc:creator","label":"Author","values":["Guo, Guannan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:37Z","2022-08-26T23:58:55Z","2020-05-11","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":["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":["Static Timing Analysis","Parallel Programming"]}]},{"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 Guannan Guo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108153"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The increasing complexity in digital design has spurred demand for faster design closure. As a primary timing measurement tool frequently used in design stage and optimization stage, static timing analysis has become one of the major performance bottlenecks in digital design. We study a novel parallel programming model and algorithm to boost timing analysis. As multi-core systems have become common in modern electronics, how to fit timing analysis into the multithreading environment is a trending research topic. We explore this direction with a new task-based multithreading framework and demonstrate its superior efficiency over existing tools. Critical path generation is a major objective timing analysis. Optimization tools always need to report on critical paths under several path constraints. We propose a general path search algorithm which can fulfill all practical path constraints and outperforms an industrial timing analysis tool. Combining the tasks proposed above, we aim to improve the efficiency of static timing analysis with both a new programming framework and new algorithm.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Guannan Guo, accepted the attached license on 2020-05-05 at 11:50.","The student, Guannan Guo, submitted this Thesis for approval on 2020-05-05 at 11:59.","This Thesis was approved for publication on 2020-05-11 at 06:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15216 on 2020-08-25 at 17:29:41","Made available in DSpace on 2020-08-26T23:58:37Z (GMT). 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As a primary timing measurement tool frequently used in design stage and optimization stage, static timing analysis has become one of the major performance bottlenecks in digital design. We study a novel parallel programming model and algorithm to boost timing analysis. As multi-core systems have become common in modern electronics, how to fit timing analysis into the multithreading environment is a trending research topic. We explore this direction with a new task-based multithreading framework and demonstrate its superior efficiency over existing tools. Critical path generation is a major objective timing analysis. Optimization tools always need to report on critical paths under several path constraints. We propose a general path search algorithm which can fulfill all practical path constraints and outperforms an industrial timing analysis tool. Combining the tasks proposed above, we aim to improve the efficiency of static timing analysis with both a new programming framework and new algorithm.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Guannan Guo, accepted the attached license on 2020-05-05 at 11:50.","The student, Guannan Guo, submitted this Thesis for approval on 2020-05-05 at 11:59.","This Thesis was approved for publication on 2020-05-11 at 06:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15216 on 2020-08-25 at 17:29:41","Made available in DSpace on 2020-08-26T23:58:37Z (GMT). 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