{"id":"https://openalex.org/W3092027164","doi":"https://doi.org/10.1109/dac18072.2020.9218643","title":"GRANNITE: Graph Neural Network Inference for Transferable Power Estimation","display_name":"GRANNITE: Graph Neural Network Inference for Transferable Power Estimation","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3092027164","doi":"https://doi.org/10.1109/dac18072.2020.9218643","mag":"3092027164"},"language":"en","primary_location":{"id":"doi:10.1109/dac18072.2020.9218643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18072.2020.9218643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 57th 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/A5100612105","display_name":"Yanqing Zhang","orcid":"https://orcid.org/0000-0003-2349-1925"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yanqing Zhang","raw_affiliation_strings":["NVIDIA, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029928585","display_name":"Haoxing Ren","orcid":"https://orcid.org/0000-0003-1028-3860"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoxing Ren","raw_affiliation_strings":["NVIDIA, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, Austin, TX, USA","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010156116","display_name":"Brucek Khailany","orcid":"https://orcid.org/0000-0002-7584-3489"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brucek Khailany","raw_affiliation_strings":["NVIDIA, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, Austin, TX, USA","institution_ids":["https://openalex.org/I4210127875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100612105"],"corresponding_institution_ids":["https://openalex.org/I4210127875"],"apc_list":null,"apc_paid":null,"fwci":6.0354,"has_fulltext":false,"cited_by_count":104,"citation_normalized_percentile":{"value":0.97090904,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9998000264167786,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9998000264167786,"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":0.9994000196456909,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/netlist","display_name":"Netlist","score":0.8859133720397949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7556065320968628},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6291984915733337},{"id":"https://openalex.org/keywords/combinational-logic","display_name":"Combinational logic","score":0.627278745174408},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47968125343322754},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46264660358428955},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4531432092189789},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45251017808914185},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4358593225479126},{"id":"https://openalex.org/keywords/logic-gate","display_name":"Logic gate","score":0.35979899764060974},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34809786081314087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24435704946517944},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.15505686402320862}],"concepts":[{"id":"https://openalex.org/C177650935","wikidata":"https://www.wikidata.org/wiki/Q1760303","display_name":"Netlist","level":2,"score":0.8859133720397949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7556065320968628},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6291984915733337},{"id":"https://openalex.org/C81409106","wikidata":"https://www.wikidata.org/wiki/Q76505","display_name":"Combinational logic","level":3,"score":0.627278745174408},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47968125343322754},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46264660358428955},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4531432092189789},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45251017808914185},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4358593225479126},{"id":"https://openalex.org/C131017901","wikidata":"https://www.wikidata.org/wiki/Q170451","display_name":"Logic gate","level":2,"score":0.35979899764060974},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34809786081314087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24435704946517944},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.15505686402320862},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac18072.2020.9218643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18072.2020.9218643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 57th ACM/IEEE Design Automation Conference (DAC)","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":7,"referenced_works":["https://openalex.org/W1983659826","https://openalex.org/W2111330726","https://openalex.org/W2133250872","https://openalex.org/W2187089797","https://openalex.org/W2899771611","https://openalex.org/W2945592068","https://openalex.org/W3213271028"],"related_works":["https://openalex.org/W2170314243","https://openalex.org/W2119179026","https://openalex.org/W4386694274","https://openalex.org/W3146543203","https://openalex.org/W2795180100","https://openalex.org/W2099346120","https://openalex.org/W2074526596","https://openalex.org/W2169337913","https://openalex.org/W4242010157","https://openalex.org/W3114476551"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"GRANNITE,":[3],"a":[4,34,39,73,86,109,117],"GPU-accelerated":[5,118],"novel":[6],"graph":[7],"neural":[8],"network":[9],"(GNN)":[10],"model":[11,65],"for":[12],"fast,":[13],"accurate,":[14],"and":[15,43,53],"transferable":[16],"vector-based":[17],"average":[18,28,69,131],"power":[19,92],"estimation.":[20],"During":[21],"training,":[22],"GRANNITE":[23,97,125],"learns":[24],"how":[25],"to":[26,90,116],"propagate":[27],"toggle":[29,56,70],"rates":[30,57,71],"through":[31],"combinational":[32,54],"logic:":[33],"netlist":[35],"is":[36],"represented":[37],"as":[38,51,60],"graph,":[40],"register":[41],"states":[42],"unit":[44],"inputs":[45],"from":[46,81],"RTL":[47,82],"simulation":[48,83],"are":[49,58],"used":[50,59],"features,":[52],"gate":[55],"labels.":[61],"A":[62],"trained":[63],"GNN":[64],"can":[66],"then":[67],"infer":[68],"on":[72],"new":[74,79],"workload":[75],"of":[76,104,112],"interest":[77],"or":[78],"netlists":[80],"results":[84],"in":[85],"few":[87],"seconds.":[88],"Compared":[89,115],"traditional":[91],"analysis":[93],"using":[94],"gate-level":[95],"simulations,":[96],"achieves":[98,126],">18.7X":[99],"speedup":[100],"with":[101],"an":[102],"error":[103],"only":[105],"<;":[106],"5.5%":[107],"across":[108],"diverse":[110],"set":[111],"benchmark":[113],"circuits.":[114],"conventional":[119],"probabilistic":[120],"switching":[121],"activity":[122],"estimation":[123],"approach,":[124],"much":[127],"better":[128],"accuracy":[129],"(on":[130],"25.9%":[132],"lower":[133],"error)":[134],"at":[135],"similar":[136],"runtimes.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
