{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108178"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108178","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Memory-centric approximate computing","abstract":"Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 WANG-THESIS-2020.pdf: 767488 bytes, checksum: f279727e3637c38ea03e13ef0926d1a7 (MD5) LICENSE.txt: 4210 bytes, checksum: b0afabfe300d734e5c194fcc53c0a560 (MD5) Previous issue date: 2020-05-12","abstract_html":"Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 WANG-THESIS-2020.pdf: 767488 bytes, checksum: f279727e3637c38ea03e13ef0926d1a7 (MD5) LICENSE.txt: 4210 bytes, checksum: b0afabfe300d734e5c194fcc53c0a560 (MD5) Previous issue date: 2020-05-12","abstract_has_math":false,"creators":["Wang, Dong Kai"],"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":["Kim, Nam Sung"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:45Z","date_published":"2020-08-26T23:58:45Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Approximate computing","Computer architecture"],"languages":["en"],"rights":["Copyright 2020 Dong Kai Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108178","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kim, Nam Sung"]},{"key":"dc:creator","label":"Author","values":["Wang, Dong Kai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:45Z","2022-08-26T23:58:55Z","2020-05-12","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":["Approximate computing","Computer architecture"]}]},{"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 Dong Kai Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108178"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 WANG-THESIS-2020.pdf: 767488 bytes, checksum: f279727e3637c38ea03e13ef0926d1a7 (MD5) LICENSE.txt: 4210 bytes, checksum: b0afabfe300d734e5c194fcc53c0a560 (MD5) Previous issue date: 2020-05-12","Embargo set by: Seth Robbins for item 115791 Lift date: 2022-08-26T23:58:55Z 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","As Moore's law continues to decline, diminishing benefits of transistor scaling necessitate a move towards specialized hardware. Approximate computing is one type of specialization that has shown promise in improving the efficiency of general-purpose processors. Fortunately, with increasing demand for data collection and processing across industry, a wide range of modern applications operate on real-world data with properties suitable for approximation. To exploit data patterns and repetitions in these applications, we propose Approximate Algebraic Memory (A2M), a specialized memory model that uses finite degree polynomials to approximate discrete ranges of memory data. A2M uses dedicated hardware to derive and store polynomial coefficients rather than memory data. In error resilient workloads, A2M can effectively reduce memory size and enable direct computation on memory content. We evaluate an on-chip implementation of A2M for general-purpose processors. Experiment results show that for CPU workloads, A2M yields minimal error (< 1%) at a fixed compression ratio of 16, and improves performance by 11.3% on average.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Dong Kai Wang, accepted the attached license on 2020-05-11 at 09:47.","The student, Dong Kai Wang, submitted this Thesis for approval on 2020-05-11 at 10:14.","This Thesis was approved for publication on 2020-05-12 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15316 on 2020-08-25 at 17:30:48"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Memory-centric approximate computing"]}]}],"canonical_facts":{"dc:contributor":["Kim, Nam Sung"],"dc:creator":["Wang, Dong Kai"],"dc:date":["2020-08-26T23:58:45Z","2022-08-26T23:58:55Z","2020-05-12","2020-05"],"dc:description":["Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 WANG-THESIS-2020.pdf: 767488 bytes, checksum: f279727e3637c38ea03e13ef0926d1a7 (MD5) LICENSE.txt: 4210 bytes, checksum: b0afabfe300d734e5c194fcc53c0a560 (MD5) Previous issue date: 2020-05-12","Embargo set by: Seth Robbins for item 115791 Lift date: 2022-08-26T23:58:55Z 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","As Moore's law continues to decline, diminishing benefits of transistor scaling necessitate a move towards specialized hardware. Approximate computing is one type of specialization that has shown promise in improving the efficiency of general-purpose processors. Fortunately, with increasing demand for data collection and processing across industry, a wide range of modern applications operate on real-world data with properties suitable for approximation. To exploit data patterns and repetitions in these applications, we propose Approximate Algebraic Memory (A2M), a specialized memory model that uses finite degree polynomials to approximate discrete ranges of memory data. A2M uses dedicated hardware to derive and store polynomial coefficients rather than memory data. In error resilient workloads, A2M can effectively reduce memory size and enable direct computation on memory content. We evaluate an on-chip implementation of A2M for general-purpose processors. Experiment results show that for CPU workloads, A2M yields minimal error (< 1%) at a fixed compression ratio of 16, and improves performance by 11.3% on average.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Dong Kai Wang, accepted the attached license on 2020-05-11 at 09:47.","The student, Dong Kai Wang, submitted this Thesis for approval on 2020-05-11 at 10:14.","This Thesis was approved for publication on 2020-05-12 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15316 on 2020-08-25 at 17:30:48"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108178"],"dc:language":["en"],"dc:rights":["Copyright 2020 Dong Kai Wang"],"dc:subject":["Approximate computing","Computer architecture"],"dc:title":["Memory-centric approximate computing"],"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":"2026-07-22T22:24:47Z"}