{"id":"https://openalex.org/W4399486107","doi":"https://doi.org/10.1145/3649476.3658744","title":"Scalable Flip-Flop Clustering Using Divide and Conquer For Capacitated K-Means","display_name":"Scalable Flip-Flop Clustering Using Divide and Conquer For Capacitated K-Means","publication_year":2024,"publication_date":"2024-06-10","ids":{"openalex":"https://openalex.org/W4399486107","doi":"https://doi.org/10.1145/3649476.3658744"},"language":"en","primary_location":{"id":"doi:10.1145/3649476.3658744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649476.3658744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2024","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/A5073558386","display_name":"Andrew B. Kahng","orcid":"https://orcid.org/0000-0002-4490-5018"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew B. Kahng","raw_affiliation_strings":["University of California, San Diego, USA"],"affiliations":[{"raw_affiliation_string":"University of California, San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004121199","display_name":"Sayak Kundu","orcid":"https://orcid.org/0000-0002-8077-1328"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sayak Kundu","raw_affiliation_strings":["University of California, San Diego, USA"],"affiliations":[{"raw_affiliation_string":"University of California, San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5092008487","display_name":"Shreyas Thumathy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shreyas Thumathy","raw_affiliation_strings":["Precision Innovations Inc., USA"],"affiliations":[{"raw_affiliation_string":"Precision Innovations Inc., USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073558386"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":1.5572,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82833826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"177","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9979000091552734,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.9976000189781189,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9958999752998352,"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/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.7617889642715454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7200044393539429},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7137171626091003},{"id":"https://openalex.org/keywords/flip-flop","display_name":"Flip-flop","score":0.6446448564529419},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6396030783653259},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4080798029899597},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36801281571388245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24722453951835632},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.061241745948791504}],"concepts":[{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.7617889642715454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7200044393539429},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7137171626091003},{"id":"https://openalex.org/C2781007278","wikidata":"https://www.wikidata.org/wiki/Q183406","display_name":"Flip-flop","level":3,"score":0.6446448564529419},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6396030783653259},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4080798029899597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36801281571388245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24722453951835632},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.061241745948791504},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3649476.3658744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649476.3658744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2024","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2346205343","https://openalex.org/W2404274178","https://openalex.org/W3110435694","https://openalex.org/W6668990524"],"related_works":["https://openalex.org/W2352794675","https://openalex.org/W1539994214","https://openalex.org/W2111712077","https://openalex.org/W2051228988","https://openalex.org/W4247094814","https://openalex.org/W108531593","https://openalex.org/W8810113","https://openalex.org/W2158636562","https://openalex.org/W2783525109","https://openalex.org/W2162911572"],"abstract_inverted_index":{"Multi-bit":[0],"flip-flop":[1,20,61,67],"clustering":[2,68],"is":[3,125],"a":[4,17,28,43,55,162],"well-studied":[5],"optimization":[6,87],"problem":[7],"in":[8,27,48,144,181],"physical":[9],"design:":[10],"carefully":[11],"merging":[12],"multiple":[13],"single-bit":[14,107,173],"flip-flops":[15],"into":[16],"single":[18],"multi-bit":[19,60],"can":[21],"decrease":[22],"the":[23,85,111],"total":[24,75,145,151,182],"power":[25,36,76,146,183],"consumption":[26],"clock":[29,35,39],"distribution":[30],"network":[31],"due":[32],"to":[33,74,104,132,157],"lower":[34],"and":[37,69,80,154],"routed":[38],"wirelength.":[40,155],"We":[41,92],"propose":[42],"pointset":[44],"decomposition":[45],"heuristic":[46],"that":[47,121],"conjunction":[49],"with":[50,72,127,149,176],"capacitated":[51],"k-means":[52],"[4]":[53],"enables":[54],"scalable,":[56],"divide-and-conquer":[57],"flow":[58,64,95,124],"for":[59],"clustering.":[62],"Our":[63],"produces":[65],"high-quality":[66],"placement":[70],"solutions":[71],"respect":[73],"consumption,":[77,147],"area,":[78],"timing,":[79],"wirelength":[81],"metrics":[82],"evaluated":[83],"after":[84],"post-routing":[86],"(PRO)":[88],"stage":[89],"of":[90,99],"P&R.":[91],"test":[93],"our":[94,122],"on":[96,166],"five":[97],"designs":[98,168],"varying":[100],"input":[101],"size":[102],"(0.5K":[103],"64K":[105],"clusterable":[106],"flip-flops)":[108],"implemented":[109],"using":[110],"ASAP7":[112],"7nm":[113],"research":[114],"enablement":[115],"[3]":[116],"[9].":[117],"Empirical":[118],"results":[119],"show":[120],"new":[123],"competitive":[126],"current":[128],"state-of-the-art":[129],"flows.":[130],"Compared":[131,156],"MeanShift":[133],"[2],":[134],"we":[135,160],"achieve":[136,161],"6.18%":[137],"(resp.":[138,141],"1.90%)":[139],"maximum":[140],"average)":[142],"reduction":[143],"along":[148],"improved":[150],"negative":[152],"slack":[153],"FlopTray":[158],"[4],":[159],"400":[163],"\u00d7":[164],"speedup":[165],"larger":[167],"such":[169],"as":[170],"VGA":[171],"(17K":[172],"flip-flops),":[174],"but":[175],"an":[177],"average":[178],"1.12%":[179],"degradation":[180],"consumption.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}