{"id":"https://openalex.org/W2614392736","doi":"https://doi.org/10.1145/3060403.3060465","title":"LightNN","display_name":"LightNN","publication_year":2017,"publication_date":"2017-05-10","ids":{"openalex":"https://openalex.org/W2614392736","doi":"https://doi.org/10.1145/3060403.3060465","mag":"2614392736"},"language":"en","primary_location":{"id":"doi:10.1145/3060403.3060465","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3060403.3060465","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3060465&type=pdf","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 2017","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3060465&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101602646","display_name":"Ruizhou Ding","orcid":"https://orcid.org/0000-0003-4311-3761"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruizhou Ding","raw_affiliation_strings":["Carnegie Mellon University, pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101673019","display_name":"Zeye Liu","orcid":"https://orcid.org/0000-0003-2516-3423"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zeye Liu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031139851","display_name":"Rongye Shi","orcid":"https://orcid.org/0000-0003-4298-9358"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongye Shi","raw_affiliation_strings":["Carnegie Mellon University, pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065985595","display_name":"Diana Marculescu","orcid":"https://orcid.org/0000-0002-5734-4221"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diana Marculescu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111967389","display_name":"R.D. Blanton","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R.D. (Shawn) Blanton","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101602646"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.4956,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.9387666,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987000226974487,"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/T10320","display_name":"Neural Networks and Applications","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/mnist-database","display_name":"MNIST database","score":0.9247920513153076},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6916748881340027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6838952898979187},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6403020620346069},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6233813166618347},{"id":"https://openalex.org/keywords/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.6038346290588379},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5668457746505737},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5254240036010742},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5144660472869873},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.49191999435424805},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.4303937554359436},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4246289134025574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3729792535305023},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.36903947591781616},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2791605591773987},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.16463682055473328},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08708417415618896}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9247920513153076},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6916748881340027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6838952898979187},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6403020620346069},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6233813166618347},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.6038346290588379},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5668457746505737},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5254240036010742},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5144660472869873},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.49191999435424805},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.4303937554359436},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4246289134025574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3729792535305023},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.36903947591781616},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2791605591773987},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.16463682055473328},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08708417415618896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3060403.3060465","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3060403.3060465","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3060465&type=pdf","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 2017","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3060403.3060465","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3060403.3060465","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3060465&type=pdf","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 2017","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G5597466768","display_name":null,"funder_award_id":"CCF 1314876, CCF 1331804","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G823147248","display_name":"Collaborative Research: CyberSEES: Climate-Aware Renewable Hydropower Generation and Disaster Avoidance","funder_award_id":"1331804","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2614392736.pdf","grobid_xml":"https://content.openalex.org/works/W2614392736.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1841592590","https://openalex.org/W1845051632","https://openalex.org/W1902934009","https://openalex.org/W1974078116","https://openalex.org/W1992348535","https://openalex.org/W1998917233","https://openalex.org/W2008684926","https://openalex.org/W2010069327","https://openalex.org/W2067658794","https://openalex.org/W2091432990","https://openalex.org/W2133218851","https://openalex.org/W2142801765","https://openalex.org/W2194775991","https://openalex.org/W2233797083","https://openalex.org/W2257979135","https://openalex.org/W2289254158","https://openalex.org/W2431931973","https://openalex.org/W2464177207","https://openalex.org/W2919115771","https://openalex.org/W2952331963","https://openalex.org/W3120740533","https://openalex.org/W4236363946","https://openalex.org/W4238869234","https://openalex.org/W4240268885","https://openalex.org/W6687483927","https://openalex.org/W6693397755","https://openalex.org/W6719768283","https://openalex.org/W6788247690"],"related_works":["https://openalex.org/W2618574054","https://openalex.org/W4385524141","https://openalex.org/W3018979822","https://openalex.org/W3026616975","https://openalex.org/W4288018014","https://openalex.org/W4297776111","https://openalex.org/W2989784533","https://openalex.org/W2996058201","https://openalex.org/W2946347869","https://openalex.org/W3127679336"],"abstract_inverted_index":{"Application-specific":[0],"integrated":[1],"circuit":[2],"(ASIC)":[3],"implementations":[4],"for":[5,77,156,167,196],"Deep":[6],"Neural":[7,57],"Networks":[8,58],"(DNNs)":[9],"have":[10,74,128,172],"been":[11],"adopted":[12],"in":[13,179],"many":[14],"systems":[15],"because":[16],"of":[17,46,118],"their":[18,40],"higher":[19],"classification":[20],"speed.":[21],"However,":[22,85],"although":[23],"they":[24],"may":[25],"be":[26],"characterized":[27],"by":[28,50,188],"better":[29,129,178],"accuracy,":[30],"larger":[31],"DNNs":[32,47,79],"require":[33],"significant":[34],"energy":[35,44,66,71,134,141],"and":[36,54,65,73,120,164,193],"area,":[37],"thereby":[38],"limiting":[39],"wide":[41],"adoption.":[42],"The":[43],"consumption":[45],"is":[48,95],"driven":[49],"both":[51],"memory":[52],"accesses":[53],"computation.":[55],"Binarized":[56],"(BNNs),":[59],"as":[60],"a":[61,91,102,115,123,132,173],"trade-off":[62],"between":[63,162],"accuracy":[64,76,89,130,147,163,180],"consumption,":[67],"can":[68],"achieve":[69],"great":[70],"reduction,":[72],"good":[75],"large":[78,168],"due":[80],"to":[81,111,159],"its":[82],"regularization":[83,174],"effect.":[84],"BNNs":[86],"show":[87],"poor":[88],"when":[90],"smaller":[92],"DNN":[93,104,125,169,198],"configuration":[94],"adopted.":[96],"In":[97],"this":[98],"paper,":[99],"we":[100],"propose":[101],"new":[103],"model,":[105],"LightNN,":[106],"which":[107],"replaces":[108],"the":[109,191],"multiplications":[110],"one":[112],"shift":[113],"or":[114],"constrained":[116],"number":[117],"shifts":[119],"adds.":[121],"For":[122],"fixed":[124],"configuration,":[126],"LightNNs":[127,152,171],"at":[131],"slight":[133],"increase":[135],"than":[136,148,181],"BNNs,":[137],"yet":[138],"are":[139,186],"more":[140,154],"efficient":[142],"with":[143],"only":[144],"slightly":[145],"less":[146],"conventional":[149,182],"DNNs.":[150,183],"Therefore,":[151],"provide":[153],"options":[155],"hardware":[157],"designers":[158],"make":[160],"trade-offs":[161],"energy.":[165],"Moreover,":[166],"configurations,":[170],"effect,":[175],"making":[176],"them":[177],"These":[184],"conclusions":[185],"verified":[187],"experiment":[189],"using":[190],"MNIST":[192],"CIFAR-10":[194],"datasets":[195],"different":[197],"configurations.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2017-05-26T00:00:00"}
