{"id":"https://openalex.org/W2613543507","doi":"https://doi.org/10.23919/date.2017.7926982","title":"Adaptive weight compression for memory-efficient neural networks","display_name":"Adaptive weight compression for memory-efficient neural networks","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2613543507","doi":"https://doi.org/10.23919/date.2017.7926982","mag":"2613543507"},"language":"en","primary_location":{"id":"doi:10.23919/date.2017.7926982","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2017.7926982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017","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/A5063908850","display_name":"Jong Hwan Ko","orcid":"https://orcid.org/0000-0003-4434-4318"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jong Hwan Ko","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041847927","display_name":"Duck-Hwan Kim","orcid":"https://orcid.org/0000-0002-6494-2182"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duckhwan Kim","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002187268","display_name":"Taesik Na","orcid":"https://orcid.org/0000-0003-2039-3772"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taesik Na","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079872127","display_name":"Jaeha Kung","orcid":"https://orcid.org/0000-0001-6151-8602"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaeha Kung","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009591041","display_name":"Saibal Mukhopadhyay","orcid":"https://orcid.org/0000-0002-8894-3390"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saibal Mukhopadhyay","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5063908850"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":1.5474,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.89879443,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"199","last_page":"204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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/T10320","display_name":"Neural Networks and Applications","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9986000061035156,"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":0.8002160787582397},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7470789551734924},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.6927542090415955},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6049378514289856},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5369216203689575},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.5076538324356079},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5055575370788574},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.47984716296195984},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.43342119455337524},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.41874706745147705},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38964566588401794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38322126865386963},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.15983691811561584},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11180400848388672},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07480037212371826},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0730580985546112}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8002160787582397},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7470789551734924},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.6927542090415955},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6049378514289856},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5369216203689575},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.5076538324356079},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5055575370788574},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.47984716296195984},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.43342119455337524},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.41874706745147705},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38964566588401794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38322126865386963},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.15983691811561584},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11180400848388672},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07480037212371826},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0730580985546112},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date.2017.7926982","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2017.7926982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8899999856948853,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1594170634","https://openalex.org/W1669276382","https://openalex.org/W1999234249","https://openalex.org/W2037379142","https://openalex.org/W2058641082","https://openalex.org/W2081547991","https://openalex.org/W2112742596","https://openalex.org/W2119144962","https://openalex.org/W2133671888","https://openalex.org/W2163605009","https://openalex.org/W2165473669","https://openalex.org/W2260663238","https://openalex.org/W2267244539","https://openalex.org/W2289252105","https://openalex.org/W2335728318","https://openalex.org/W2382313035","https://openalex.org/W2399948372","https://openalex.org/W2471695703","https://openalex.org/W2481166585","https://openalex.org/W2518511512","https://openalex.org/W2618530766","https://openalex.org/W2964299589","https://openalex.org/W3003694668","https://openalex.org/W3024621361","https://openalex.org/W4239722617","https://openalex.org/W6635557998","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6703116779","https://openalex.org/W6821704442"],"related_works":["https://openalex.org/W2441603424","https://openalex.org/W2542654834","https://openalex.org/W4243959093","https://openalex.org/W1994319845","https://openalex.org/W2971784143","https://openalex.org/W2159814397","https://openalex.org/W3095399624","https://openalex.org/W1968318766","https://openalex.org/W4312760377","https://openalex.org/W2357838320"],"abstract_inverted_index":{"Neural":[0],"networks":[1],"generally":[2],"require":[3],"significant":[4],"memory":[5,90,136],"capacity/bandwidth":[6],"to":[7,24,46,51,120],"store/access":[8],"a":[9],"large":[10],"number":[11],"of":[12,20,35,43,57,66,102,128],"synaptic":[13],"weights.":[14],"This":[15],"paper":[16],"presents":[17],"an":[18],"application":[19],"JPEG":[21,47,59],"image":[22],"encoding":[23],"compress":[25],"the":[26,30,36,41,54,58,63,70,74,115],"weights":[27],"by":[28],"exploiting":[29],"spatial":[31],"locality":[32],"and":[33,99,138],"smoothness":[34],"weight":[37,75],"matrix.":[38],"To":[39],"minimize":[40],"loss":[42,127],"accuracy":[44],"due":[45],"encoding,":[48],"we":[49],"propose":[50],"adaptively":[52],"control":[53],"quantization":[55],"factor":[56],"algorithm":[60],"depending":[61],"on":[62],"error-sensitivity":[64],"(gradient)":[65],"each":[67],"weight.":[68],"With":[69],"adaptive":[71,87],"compression":[72,88,122],"technique,":[73],"blocks":[76],"with":[77,114,123],"higher":[78,84,97,134],"sensitivity":[79],"are":[80],"compressed":[81],"less":[82,124],"for":[83,108,111],"accuracy.":[85],"The":[86,106],"reduces":[89],"requirement,":[91],"which":[92],"in":[93,96,132],"turn":[94],"results":[95],"performance":[98],"lower":[100,140],"energy":[101],"neural":[103],"network":[104],"hardware.":[105],"simulation":[107],"inference":[109],"hardware":[110],"multilayer":[112],"perceptron":[113],"MNIST":[116],"dataset":[117],"shows":[118],"up":[119],"42X":[121],"than":[125],"1%":[126],"recognition":[129],"accuracy,":[130],"resulting":[131],"3X":[133],"effective":[135],"bandwidth":[137],"~19X":[139],"system":[141],"energy.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}