{"id":"https://openalex.org/W2982317364","doi":"https://doi.org/10.1109/jetcas.2019.2950093","title":"EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators","display_name":"EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2982317364","doi":"https://doi.org/10.1109/jetcas.2019.2950093","mag":"2982317364"},"language":"en","primary_location":{"id":"pmh:oai:cris.unibo.it:11585/728412","is_oa":true,"landing_page_url":"http://hdl.handle.net/11585/728412","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11585/728412","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025399641","display_name":"Lukas Cavigelli","orcid":"https://orcid.org/0000-0003-1767-7715"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Cavigelli L.","raw_affiliation_strings":["Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007387832","display_name":"Georg Rutishauser","orcid":"https://orcid.org/0000-0001-8875-7611"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Rutishauser G.","raw_affiliation_strings":["Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043408422","display_name":"Luca Benini","orcid":"https://orcid.org/0000-0001-8068-3806"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["CH","IT"],"is_corresponding":false,"raw_author_name":"Benini L.","raw_affiliation_strings":["Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy","Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]},{"raw_affiliation_string":"Integrated Systems Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0337,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.89938117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9983999729156494,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7609824538230896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7046372890472412},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.5565351247787476},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5524491667747498},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.49686482548713684},{"id":"https://openalex.org/keywords/bit-plane","display_name":"Bit plane","score":0.483134001493454},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.47285178303718567},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4710150957107544},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4675672650337219},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4409607946872711},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.4387105107307434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40520280599594116},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34018415212631226},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.33045119047164917},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.1418519914150238},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12064388394355774}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609824538230896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7046372890472412},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.5565351247787476},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5524491667747498},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.49686482548713684},{"id":"https://openalex.org/C56056324","wikidata":"https://www.wikidata.org/wiki/Q878866","display_name":"Bit plane","level":4,"score":0.483134001493454},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.47285178303718567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4710150957107544},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4675672650337219},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4409607946872711},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4387105107307434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40520280599594116},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34018415212631226},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.33045119047164917},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.1418519914150238},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12064388394355774},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C183383400","wikidata":"https://www.wikidata.org/wiki/Q2374485","display_name":"Bit field","level":3,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C25197100","wikidata":"https://www.wikidata.org/wiki/Q890886","display_name":"Drilling","level":2,"score":0.0},{"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/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:cris.unibo.it:11585/728412","is_oa":true,"landing_page_url":"http://hdl.handle.net/11585/728412","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:cris.unibo.it:11585/728412","is_oa":true,"landing_page_url":"http://hdl.handle.net/11585/728412","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1816877561","display_name":null,"funder_award_id":"732631 (OPRECOMP)","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1563795667","https://openalex.org/W1677182931","https://openalex.org/W1902934009","https://openalex.org/W1990653637","https://openalex.org/W2070167224","https://openalex.org/W2091902424","https://openalex.org/W2119144962","https://openalex.org/W2122962290","https://openalex.org/W2162310490","https://openalex.org/W2172166488","https://openalex.org/W2194775991","https://openalex.org/W2260498192","https://openalex.org/W2279098554","https://openalex.org/W2285660444","https://openalex.org/W2404464147","https://openalex.org/W2431931973","https://openalex.org/W2437875770","https://openalex.org/W2442974303","https://openalex.org/W2487148512","https://openalex.org/W2516109628","https://openalex.org/W2516141709","https://openalex.org/W2559085405","https://openalex.org/W2565851976","https://openalex.org/W2582144945","https://openalex.org/W2592929672","https://openalex.org/W2606822607","https://openalex.org/W2623629680","https://openalex.org/W2625457103","https://openalex.org/W2732044853","https://openalex.org/W2741232386","https://openalex.org/W2787697768","https://openalex.org/W2788838111","https://openalex.org/W2796347433","https://openalex.org/W2819476901","https://openalex.org/W2883780447","https://openalex.org/W2889339831","https://openalex.org/W2904327456","https://openalex.org/W2909953480","https://openalex.org/W2946521496","https://openalex.org/W2960643108","https://openalex.org/W2962821792","https://openalex.org/W2962836170","https://openalex.org/W2962840532","https://openalex.org/W2963087201","https://openalex.org/W2963114950","https://openalex.org/W2963145956","https://openalex.org/W2963163009","https://openalex.org/W2963258090","https://openalex.org/W2963439282","https://openalex.org/W2963918968","https://openalex.org/W2964228333","https://openalex.org/W2964299589","https://openalex.org/W2966206672","https://openalex.org/W2997787774","https://openalex.org/W3098490553","https://openalex.org/W3121517425","https://openalex.org/W4240168186","https://openalex.org/W4242874873","https://openalex.org/W4293584584"],"related_works":["https://openalex.org/W3210332869","https://openalex.org/W2082705439","https://openalex.org/W2357231169","https://openalex.org/W4327499886","https://openalex.org/W2549477129","https://openalex.org/W4382998692","https://openalex.org/W2945911613","https://openalex.org/W405334833","https://openalex.org/W2088378984","https://openalex.org/W3080614128"],"abstract_inverted_index":{"In":[0],"the":[1,4,18,89,104,120,151,161,214],"wake":[2],"of":[3,6,57,88,130,145,153,171,194],"success":[5],"convolutional":[7,109],"neural":[8,110],"networks":[9],"in":[10,46,124],"image":[11],"classification,":[12],"object":[13],"recognition,":[14,16],"speech":[15],"etc.,":[17],"demand":[19],"for":[20,43,103,119,174,177,181,186,204,221,232],"deploying":[21],"these":[22],"compute-intensive":[23],"ML":[24],"models":[25],"on":[26,213],"embedded":[27],"and":[28,34,81,94,99,116,122,142,183,217],"mobile":[29],"systems":[30],"with":[31],"tight":[32],"power":[33,71],"energy":[35],"constraints":[36],"at":[37,134,160],"low":[38,210],"cost,":[39],"as":[40,42],"well":[41],"boosting":[44],"throughput":[45,129],"data":[47],"centers,":[48],"is":[49,65,73],"growing":[50],"rapidly.":[51],"This":[52],"has":[53,208],"sparked":[54],"a":[55,85,96,128,192,209],"surge":[56],"research":[58],"into":[59,139],"specialized":[60],"hardware":[61,114],"accelerators.":[62],"Their":[63],"performance":[64],"typically":[66],"limited":[67],"by":[68,75],"I/O":[69,76],"bandwidth,":[70],"consumption":[72],"dominated":[74],"transfers":[77],"to":[78],"off-chip":[79],"memory,":[80],"on-chip":[82],"memories":[83],"occupy":[84],"large":[86],"part":[87],"silicon":[90,146],"area.":[91],"We":[92,112,164],"introduce":[93],"evaluate":[95],"novel,":[97],"hardware-friendly,":[98],"lossless":[100],"compression":[101,169,215,219,224],"scheme":[102],"feature":[105],"maps":[106],"present":[107,113],"within":[108],"networks.":[111],"architectures":[115],"synthesis":[117],"results":[118],"compressor":[121],"decompressor":[123],"65":[125],"nm.":[126],"With":[127],"one":[131],"8-bit":[132,157],"word/cycle":[133],"600":[135],"MHz,":[136],"they":[137],"fit":[138],"2.8":[140],"kGE":[141,144],"3.0":[143],"area,":[147],"respectively":[148],"-":[149,191],"together":[150],"size":[152],"less":[154],"than":[155,231],"seven":[156],"multiply-add":[158],"units":[159],"same":[162],"throughput.":[163],"show":[165],"that":[166,227],"an":[167],"average":[168],"ratio":[170],"5.1":[172],"\u00d7":[173,180,185],"AlexNet,":[175],"4":[176],"VGG-16,":[178],"2.4":[179],"ResNet-34":[182],"2.2":[184],"MobileNetV2":[187],"can":[188],"be":[189],"achieved":[190],"gain":[193],"45-70%":[195],"over":[196],"existing":[197],"methods.":[198],"Our":[199],"approach":[200],"also":[201],"works":[202],"effectively":[203],"various":[205],"number":[206],"formats,":[207],"frame-to-frame":[211],"variance":[212],"ratio,":[216],"achieves":[218],"factors":[220],"gradient":[222],"map":[223],"during":[225],"training":[226],"are":[228],"even":[229],"better":[230],"inference.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7}],"updated_date":"2026-06-12T08:23:45.883708","created_date":"2025-10-10T00:00:00"}