{"id":"https://openalex.org/W2945384718","doi":"https://doi.org/10.1145/3316781.3317896","title":"Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models","display_name":"Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models","publication_year":2019,"publication_date":"2019-05-23","ids":{"openalex":"https://openalex.org/W2945384718","doi":"https://doi.org/10.1145/3316781.3317896","mag":"2945384718"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5038501242","display_name":"Mohamed Baker Alawieh","orcid":"https://orcid.org/0000-0002-3546-0336"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohamed Baker Alawieh","raw_affiliation_strings":["Electrical and Computer Engineering Department, UT Austin"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, UT Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090272445","display_name":"Sinead A. Williamson","orcid":"https://orcid.org/0000-0002-0572-0045"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sinead A. Williamson","raw_affiliation_strings":["Information, Risk and Operations Management & Statistics and Data Science, UT Austin and Amazon.com Inc."],"affiliations":[{"raw_affiliation_string":"Information, Risk and Operations Management & Statistics and Data Science, UT Austin and Amazon.com Inc.","institution_ids":["https://openalex.org/I1311688040","https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011883763","display_name":"David Z. Pan","orcid":"https://orcid.org/0000-0002-5705-2501"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Z. Pan","raw_affiliation_strings":["Electrical and Computer Engineering Department, UT Austin"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, UT Austin","institution_ids":["https://openalex.org/I86519309"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.46,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":6,"citation_normalized_percentile":{"value":0.571549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":81,"max":82},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9993,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9993,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9986,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9985,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.71006966},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46211627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7907933},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.71006966},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.6059905},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5646253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48433077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47797272},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46211627},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4465461},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4269328},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4113891},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3456236},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":22,"referenced_works":["https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1969415786","https://openalex.org/W1970441097","https://openalex.org/W1989728241","https://openalex.org/W2047028564","https://openalex.org/W2049039038","https://openalex.org/W2053430356","https://openalex.org/W2100992121","https://openalex.org/W2102649620","https://openalex.org/W2112894859","https://openalex.org/W2135046866","https://openalex.org/W2141004763","https://openalex.org/W2155811837","https://openalex.org/W2344478748","https://openalex.org/W2403278033","https://openalex.org/W2625568424","https://openalex.org/W2782159825","https://openalex.org/W2908948901","https://openalex.org/W2913204915","https://openalex.org/W2913205331","https://openalex.org/W4234698323"],"related_works":["https://openalex.org/W4386190339","https://openalex.org/W4292122269","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2968424575","https://openalex.org/W2949366006","https://openalex.org/W2580650124","https://openalex.org/W2164129707","https://openalex.org/W2111669074"],"abstract_inverted_index":{"As":[0],"integrated":[1],"circuit":[2],"technologies":[3],"continue":[4],"to":[5,20,45,58,106,129],"scale,":[6],"efficient":[7,47],"performance":[8,28,48],"modeling":[9,63,132],"becomes":[10],"indispensable.":[11],"Recently,":[12],"several":[13],"new":[14,56],"learning":[15],"paradigms":[16,37],"have":[17],"been":[18],"proposed":[19,75,122],"reduce":[21],"the":[22,39,42,62,82,108,113,121,140,144],"computational":[23],"cost":[24],"associated":[25],"with":[26],"accurate":[27],"modeling.":[29],"A":[30],"common":[31],"attribute":[32],"among":[33],"most":[34],"of":[35,41],"these":[36],"is":[38,91],"leverage":[40],"sparsity":[43,60],"feature":[44,70],"build":[46],"models.":[49],"In":[50],"this":[51],"work,":[52],"we":[53],"propose":[54],"a":[55,94,102],"perspective":[57],"incorporate":[59],"in":[61,143],"task":[64],"by":[65],"utilizing":[66],"spike":[67],"and":[68,111],"slab":[69],"selection":[71],"techniques.":[72],"Practically,":[73],"our":[74],"method":[76],"uses":[77],"two":[78],"different":[79,83],"priors":[80],"on":[81,87],"model":[84,96,114],"coefficients":[85],"based":[86],"their":[88],"importance.":[89],"This":[90],"incorporated":[92],"into":[93],"mixture":[95],"that":[97,120],"can":[98,124],"be":[99],"built":[100],"using":[101],"hierarchical":[103],"Bayesian":[104],"framework":[105],"select":[107],"important":[109,141],"features":[110,142],"find":[112],"coefficients.":[115],"Our":[116],"numerical":[117],"experiments":[118],"demonstrate":[119],"approach":[123],"achieve":[125],"better":[126],"results":[127],"compared":[128],"traditional":[130],"sparse":[131],"techniques":[133],"while":[134],"also":[135],"providing":[136],"valuable":[137],"insight":[138],"about":[139],"model.":[145]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2945384718","counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2024-12-09T07:55:55.662258","created_date":"2019-05-29"}