{"id":"https://openalex.org/W3212743974","doi":"https://doi.org/10.1109/dac18074.2021.9586230","title":"SLAP: A Supervised Learning Approach for Priority Cuts Technology Mapping","display_name":"SLAP: A Supervised Learning Approach for Priority Cuts Technology Mapping","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3212743974","doi":"https://doi.org/10.1109/dac18074.2021.9586230","mag":"3212743974"},"language":"en","primary_location":{"id":"doi:10.1109/dac18074.2021.9586230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028824039","display_name":"Walter Lau Neto","orcid":"https://orcid.org/0000-0002-9349-4964"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Walter Lau Neto","raw_affiliation_strings":["University of Utah, Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101963901","display_name":"Matheus T. Moreira","orcid":"https://orcid.org/0000-0001-5030-9215"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matheus T. Moreira","raw_affiliation_strings":["Chronos Tech, San Diego, California, USA"],"affiliations":[{"raw_affiliation_string":"Chronos Tech, San Diego, California, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425072","display_name":"Yingjie Li","orcid":"https://orcid.org/0000-0002-5851-7331"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingjie Li","raw_affiliation_strings":["University of Utah, Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023405941","display_name":"Luca Amar\u00f9","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088951","display_name":"Synopsys (United States)","ror":"https://ror.org/013by2m91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210088951"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luca Amaru","raw_affiliation_strings":["Synopsys Inc., Design Group, Sunnyvale, California, USA"],"affiliations":[{"raw_affiliation_string":"Synopsys Inc., Design Group, Sunnyvale, California, USA","institution_ids":["https://openalex.org/I4210088951"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029321729","display_name":"Cunxi Yu","orcid":"https://orcid.org/0000-0003-3481-307X"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cunxi Yu","raw_affiliation_strings":["University of Utah, Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002568331","display_name":"Pierre\u2010Emmanuel Gaillardon","orcid":"https://orcid.org/0000-0003-3634-3999"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pierre-Emmanuel Gaillardon","raw_affiliation_strings":["University of Utah, Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5028824039"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":2.7081,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.91122161,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"859","last_page":"864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7605928182601929},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7474400997161865},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.7407870292663574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5402092933654785},{"id":"https://openalex.org/keywords/electronic-design-automation","display_name":"Electronic design automation","score":0.5242936015129089},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5058676600456238},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.49443554878234863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49226632714271545},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4429016709327698},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4265984892845154},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.4264160692691803},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.10949349403381348},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09606724977493286}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605928182601929},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7474400997161865},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7407870292663574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5402092933654785},{"id":"https://openalex.org/C64260653","wikidata":"https://www.wikidata.org/wiki/Q1194864","display_name":"Electronic design automation","level":2,"score":0.5242936015129089},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5058676600456238},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.49443554878234863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49226632714271545},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4429016709327698},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4265984892845154},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.4264160692691803},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.10949349403381348},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09606724977493286},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac18074.2021.9586230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1776822698","https://openalex.org/W1985457698","https://openalex.org/W1996198117","https://openalex.org/W2007701500","https://openalex.org/W2035561773","https://openalex.org/W2096466247","https://openalex.org/W2100465945","https://openalex.org/W2100955320","https://openalex.org/W2112173236","https://openalex.org/W2242458479","https://openalex.org/W2346205343","https://openalex.org/W2607264901","https://openalex.org/W2612536903","https://openalex.org/W2788447492","https://openalex.org/W2797469317","https://openalex.org/W2945560322","https://openalex.org/W2946189651","https://openalex.org/W2962949351","https://openalex.org/W2975468520","https://openalex.org/W2997292582","https://openalex.org/W3013938122","https://openalex.org/W3036033727","https://openalex.org/W3111604831","https://openalex.org/W3127733006","https://openalex.org/W4239323126","https://openalex.org/W4240866746","https://openalex.org/W6736495275","https://openalex.org/W6750384299","https://openalex.org/W6770320521"],"related_works":["https://openalex.org/W2280422768","https://openalex.org/W3143197806","https://openalex.org/W4252555497","https://openalex.org/W3121175838","https://openalex.org/W3016293053","https://openalex.org/W2401723157","https://openalex.org/W2952904874","https://openalex.org/W4297690216","https://openalex.org/W3137434606","https://openalex.org/W4372263373"],"abstract_inverted_index":{"Recently":[0],"we":[1,43,125,147],"have":[2],"seen":[3],"many":[4],"works":[5],"that":[6],"leverage":[7],"Machine":[8],"Learning":[9],"(ML)":[10],"techniques":[11,24],"in":[12,87],"optimizing":[13],"Electronic":[14],"Design":[15],"Automation":[16],"(EDA)":[17],"process.":[18],"However,":[19],"the":[20,94,98,117,123,138,156],"uses":[21],"of":[22,31,36,60,69,82,89,140,152],"ML":[23,55,157],"are":[25],"limited":[26],"to":[27,93,116],"learning":[28],"forecasting":[29],"models":[30],"existing":[32],"EDA":[33],"algorithms,":[34],"instead":[35],"developing":[37],"novel":[38,48],"algorithms.":[39],"In":[40],"this":[41],"work,":[42],"focus":[44],"on":[45,144],"designing":[46],"an":[47],"cut-based":[49],"technology":[50],"mapping":[51],"algorithms":[52],"assisted":[53],"by":[54,104,155,159],"techniques,":[56],"which":[57],"matches":[58],"results":[59,130],"exhaustive":[61,118],"cut":[62],"exploration":[63],"but":[64],"preserving":[65],"a":[66,79,111,149],"small":[67],"footprint":[68],"utilized":[70,91],"cuts.":[71],"The":[72],"proposed":[73],"approach":[74],"has":[75],"been":[76],"demonstrated":[77],"with":[78,84,110],"wide":[80],"range":[81],"benchmarks":[83],"24%":[85],"reductions":[86],"number":[88,139],"cuts":[90,142],"compared":[92],"state-of-the-art,":[95],"while":[96,131],"improving":[97],"circuit":[99],"delay,":[100],"and":[101],"Area-Delay-Product":[102],"(ADP),":[103],"average":[105],"about":[106],"10%,":[107],"7%,":[108],"respectively,":[109],"2%":[112],"area":[113],"penalty.":[114],"Compared":[115],"approach,":[119],"i.e.,":[120],"considering":[121],"all":[122],"cuts,":[124],"achieve":[126],"similar":[127],"or":[128],"better":[129],"saving":[132],"over":[133],"than":[134],"$2":[135],"\\times":[136],"$":[137],"considered":[141],"(runtime)":[143],"average.":[145],"Finally,":[146],"provide":[148],"comprehensive":[150],"explanation":[151],"heuristics":[153],"learned":[154],"model":[158],"feature":[160],"ranking.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}