{"id":"https://openalex.org/W4288083644","doi":"https://doi.org/10.1145/3373087.3375306","title":"AutoDNNchip","display_name":"AutoDNNchip","publication_year":2020,"publication_date":"2020-02-23","ids":{"openalex":"https://openalex.org/W4288083644","doi":"https://doi.org/10.1145/3373087.3375306"},"language":"en","primary_location":{"id":"doi:10.1145/3373087.3375306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373087.3375306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373087.3375306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3373087.3375306","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100600605","display_name":"Pengfei Xu","orcid":"https://orcid.org/0000-0002-7304-734X"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pengfei Xu","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330729","display_name":"Xiaofan Zhang","orcid":"https://orcid.org/0000-0001-5081-3972"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaofan Zhang","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053022655","display_name":"Cong Hao","orcid":"https://orcid.org/0000-0002-2541-8767"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cong Hao","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107593559","display_name":"Yang Zhao","orcid":"https://orcid.org/0000-0001-8023-1551"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Zhao","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101912939","display_name":"Yongan Zhang","orcid":"https://orcid.org/0000-0001-7919-049X"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongan Zhang","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701547","display_name":"Yue Wang","orcid":"https://orcid.org/0000-0001-5889-0729"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Wang","raw_affiliation_strings":["Rice University, Houson, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houson, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065172226","display_name":"Chaojian Li","orcid":"https://orcid.org/0000-0003-4030-9777"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaojian Li","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025414128","display_name":"Zetong Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zetong Guan","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056321228","display_name":"Deming Chen","orcid":"https://orcid.org/0000-0002-3016-0270"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deming Chen","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019582323","display_name":"Yingyan Lin","orcid":"https://orcid.org/0000-0001-5946-203X"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingyan Lin","raw_affiliation_strings":["Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5100600605"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":5.2993,"has_fulltext":true,"cited_by_count":72,"citation_normalized_percentile":{"value":0.96682512,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"50"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9983999729156494,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9948999881744385,"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/computer-science","display_name":"Computer science","score":0.7842225432395935},{"id":"https://openalex.org/keywords/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.6793230772018433},{"id":"https://openalex.org/keywords/design-space-exploration","display_name":"Design space exploration","score":0.6542607545852661},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.617755651473999},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.5702998042106628},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.5075467824935913},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.486524760723114},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4593219757080078},{"id":"https://openalex.org/keywords/system-on-a-chip","display_name":"System on a chip","score":0.43574631214141846},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.4131245017051697},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3328758478164673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7842225432395935},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.6793230772018433},{"id":"https://openalex.org/C2776221188","wikidata":"https://www.wikidata.org/wiki/Q21072556","display_name":"Design space exploration","level":2,"score":0.6542607545852661},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.617755651473999},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.5702998042106628},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.5075467824935913},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.486524760723114},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4593219757080078},{"id":"https://openalex.org/C118021083","wikidata":"https://www.wikidata.org/wiki/Q610398","display_name":"System on a chip","level":2,"score":0.43574631214141846},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.4131245017051697},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3328758478164673},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3373087.3375306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373087.3375306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373087.3375306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2001.03535","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.03535","pdf_url":"https://arxiv.org/pdf/2001.03535","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3373087.3375306","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373087.3375306","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373087.3375306","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4612306153","display_name":null,"funder_award_id":"1937592","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316786","display_name":"Center for Cognitive Computing Systems Research","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4288083644.pdf","grobid_xml":"https://content.openalex.org/works/W4288083644.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W118713324","https://openalex.org/W1686810756","https://openalex.org/W1982052956","https://openalex.org/W2094756095","https://openalex.org/W2137966900","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2289252105","https://openalex.org/W2396572963","https://openalex.org/W2519224033","https://openalex.org/W2559655401","https://openalex.org/W2566876368","https://openalex.org/W2604272474","https://openalex.org/W2606722458","https://openalex.org/W2613718673","https://openalex.org/W2727238169","https://openalex.org/W2737762472","https://openalex.org/W2759777444","https://openalex.org/W2762910930","https://openalex.org/W2801037557","https://openalex.org/W2802331610","https://openalex.org/W2804193803","https://openalex.org/W2860338957","https://openalex.org/W2887782013","https://openalex.org/W2899915146","https://openalex.org/W2906043559","https://openalex.org/W2940862705","https://openalex.org/W2957270119","https://openalex.org/W2962856739","https://openalex.org/W2963163009","https://openalex.org/W2964010167","https://openalex.org/W2974412363","https://openalex.org/W2990765879","https://openalex.org/W2997929983","https://openalex.org/W3010833785","https://openalex.org/W3037913581","https://openalex.org/W4247470470","https://openalex.org/W4387007199","https://openalex.org/W6676297131","https://openalex.org/W6756333796","https://openalex.org/W6770166933"],"related_works":["https://openalex.org/W1485756991","https://openalex.org/W2376218453","https://openalex.org/W2984236338","https://openalex.org/W3005710104","https://openalex.org/W3092334294","https://openalex.org/W4313341326","https://openalex.org/W2518118925","https://openalex.org/W2290315036","https://openalex.org/W1828239946","https://openalex.org/W2171872191"],"abstract_inverted_index":{"Recent":[0],"breakthroughs":[1],"in":[2,157],"Deep":[3],"Neural":[4],"Networks":[5],"(DNNs)":[6],"have":[7,29,69],"fueled":[8],"a":[9,70,99,117,150,168,177,200],"growing":[10],"demand":[11],"for":[12,63,149],"domain-specific":[13],"hardware":[14,77,191],"accelerators":[15,272],"(i.e.,":[16,132],"DNN":[17,21,66,88,110,118,129,178,188,211,262,271],"chips).":[18],"However,":[19],"designing":[20],"chips":[22,89,212],"is":[23,41,58],"non-trivial":[24],"because:":[25],"(1)":[26,167],"mainstream":[27],"DNNs":[28,141],"millions":[30],"of":[31,35,48,102,163,210,287,297],"parameters":[32],"and":[33,55,79,97,108,127,153,174,183,195,198,228,264,268,291],"billions":[34],"operations;":[36],"(2)":[37,199],"the":[38,64,158,187,207,225,239,295],"design":[39,46,96,208,241],"space":[40,209],"large":[42,100],"due":[43],"to":[44,68,82,93,95,237,281],"numerous":[45],"choices":[47],"dataflows,":[49],"processing":[50],"elements,":[51],"memory":[52],"hierarchy,":[53],"etc.;":[54],"(3)":[56],"there":[57],"an":[59],"algorithm/hardware":[60],"co-design":[61],"need":[62],"same":[65],"functionality":[67],"different":[71,76,84],"decomposition":[72],"that":[73,121,246,286],"would":[74],"require":[75,98],"IPs":[78],"thus":[80],"correspond":[81],"dramatically":[83],"performance/energy/area":[85],"tradeoffs.":[86],"Therefore,":[87],"often":[90],"take":[91],"months":[92],"years":[94],"team":[101],"cross-disciplinary":[103],"experts.":[104],"To":[105],"enable":[106],"fast":[107],"effective":[109],"chip":[111,119,130,222],"design,":[112],"we":[113],"propose":[114],"AutoDNNchip":[115,161,276],"-":[116],"generator":[120],"can":[122,172,204,277,302],"automatically":[123,205],"produce":[124],"both":[125],"FPGA-":[126,290],"ASIC-based":[128,292],"implementation":[131],"synthesizable":[133,231],"RTL":[134,232],"code":[135,233,301],"with":[136,234],"optimized":[137,235],"algorithm-to-hardware":[138],"mapping)":[139],"from":[140,253],"developed":[142],"by":[143,256,274],"machine":[144],"learning":[145],"frameworks":[146],"(e.g.,":[147],"PyTorch)":[148],"designated":[151],"application":[152],"dataset":[154],"without":[155],"humans":[156],"loop.":[159],"Specifically,":[160],"consists":[162],"2":[164],"integrated":[165],"enablers:":[166],"Chip":[169,201,226,248],"Predictor,":[170,227],"which":[171,203],"accurately":[173],"efficiently":[175],"predict":[176],"accelerator's":[179],"energy,":[180],"throughput,":[181],"latency,":[182],"area":[184],"based":[185],"on":[186],"model":[189],"parameters,":[190],"configurations,":[192,217],"technology-based":[193],"IPs,":[194],"platform":[196],"constraints;":[197],"Builder,":[202],"explore":[206],"(including":[213],"IP":[214],"selections,":[215],"block":[216],"resource":[218],"balancing,":[219],"etc.),":[220],"optimize":[221],"designs":[223],"via":[224],"then":[229],"generate":[230],"dataflows":[236],"achieve":[238,278],"target":[240],"metrics.":[242],"Experimental":[243],"results":[244],"show":[245],"our":[247,275],"Predictor's":[249],"predicted":[250],"performance":[251,284],"differs":[252],"real-measured":[254],"ones":[255],"<10%":[257],"when":[258],"validated":[259],"using":[260],"15":[261],"models":[263],"4":[265],"platforms":[266],"(edge-FPGA/TPU/GPU":[267],"ASIC).":[269],"Furthermore,":[270],"generated":[273],"better":[279],"(up":[280],"3.86X":[282],"improvement)":[283],"than":[285],"expert-crafted":[288],"state-of-the-art":[289],"accelerators,":[293],"showing":[294],"effectiveness":[296],"AutoDNNchip.":[298],"Our":[299],"open-source":[300],"be":[303],"found":[304],"at":[305],"https://github.com/RICE-EIC/AutoDNNchip.git.":[306]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":5}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-07-28T00:00:00"}