{"id":"https://openalex.org/W3090718166","doi":"https://doi.org/10.3233/faia200286","title":"Towards lossless binary convolutional neural networks using piecewise approximation","display_name":"Towards lossless binary convolutional neural networks using piecewise approximation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3090718166","doi":"https://doi.org/10.3233/faia200286","mag":"3090718166"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/ee32a661-22b0-47b0-af04-f25e0a70da76","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/ee32a661-22b0-47b0-af04-f25e0a70da76","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Zhu, B, Al-Ars, Z & Pan, W 2020, Towards lossless binary convolutional neural networks using piecewise approximation. in G De Giacomo, A Catala, B Dilkina, M Milano, S Barro, A Bugarin & J Lang (eds), ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 325, IOS Press, pp. 1730-1737, 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020, Santiago de Compostela, Online, Spain, 29/08/20. https://doi.org/10.3233/FAIA200286","raw_type":"info:eu-repo/semantics/conferenceObject"},"type":"book-chapter","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://resolver.tudelft.nl/uuid:b760b9cc-c61b-4954-9033-15a426f5b550","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100769744","display_name":"Baozhou Zhu","orcid":"https://orcid.org/0000-0002-6565-2419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, B. (author)","raw_affiliation_strings":["Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021955713","display_name":"Zaid Al-Ars","orcid":"https://orcid.org/0000-0001-7670-8572"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Al-Ars, Z. (author)","raw_affiliation_strings":["Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001958960","display_name":"Pan Wei","orcid":"https://orcid.org/0000-0002-4782-5777"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, W. (author)","raw_affiliation_strings":["Robot Dynamics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robot Dynamics","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.127,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52428366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"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":0.8198999762535095,"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":0.8198999762535095,"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/T14347","display_name":"Big Data and Digital Economy","score":0.016899999231100082,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12676","display_name":"Machine Learning and ELM","score":0.015699999406933784,"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/computer-science","display_name":"Computer science","score":0.7446670532226562},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6835492253303528},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.6448174715042114},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.5636118054389954},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5013751983642578},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.426575630903244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39657676219940186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2935176193714142},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16372361779212952},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.14787524938583374}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446670532226562},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6835492253303528},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.6448174715042114},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.5636118054389954},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5013751983642578},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.426575630903244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39657676219940186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2935176193714142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16372361779212952},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.14787524938583374},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/ee32a661-22b0-47b0-af04-f25e0a70da76","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/ee32a661-22b0-47b0-af04-f25e0a70da76","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Zhu, B, Al-Ars, Z & Pan, W 2020, Towards lossless binary convolutional neural networks using piecewise approximation. in G De Giacomo, A Catala, B Dilkina, M Milano, S Barro, A Bugarin & J Lang (eds), ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 325, IOS Press, pp. 1730-1737, 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020, Santiago de Compostela, Online, Spain, 29/08/20. https://doi.org/10.3233/FAIA200286","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:tudelft.nl:uuid:b760b9cc-c61b-4954-9033-15a426f5b550","is_oa":true,"landing_page_url":"http://resolver.tudelft.nl/uuid:b760b9cc-c61b-4954-9033-15a426f5b550","pdf_url":"http://resolver.tudelft.nl/uuid:b760b9cc-c61b-4954-9033-15a426f5b550","source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"book chapter"}],"best_oa_location":{"id":"pmh:oai:tudelft.nl:uuid:b760b9cc-c61b-4954-9033-15a426f5b550","is_oa":true,"landing_page_url":"http://resolver.tudelft.nl/uuid:b760b9cc-c61b-4954-9033-15a426f5b550","pdf_url":"http://resolver.tudelft.nl/uuid:b760b9cc-c61b-4954-9033-15a426f5b550","source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"book chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3090718166.pdf","grobid_xml":"https://content.openalex.org/works/W3090718166.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4136762","https://openalex.org/W10260944","https://openalex.org/W5056303","https://openalex.org/W14994697","https://openalex.org/W12874744","https://openalex.org/W3404255","https://openalex.org/W6824668","https://openalex.org/W4671456","https://openalex.org/W2849812","https://openalex.org/W14781500"],"abstract_inverted_index":{"<p>Binary":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(CNNs)":[4],"can":[5,120,169],"significantly":[6],"reduce":[7,121],"the":[8,14,21,33,91,97,138,141],"number":[9],"of":[10,16,23,36,82,140],"arithmetic":[11],"operations":[12,84],"and":[13,38,47,76,79,101,103,124,151,162,177,196],"size":[15],"memory":[17,149],"storage,":[18],"which":[19,67,185],"makes":[20],"deployment":[22],"CNNs":[24,41,66,158],"on":[25,113,118],"mobile":[26],"or":[27],"embedded":[28],"systems":[29],"more":[30],"promising.":[31],"However,":[32],"accuracy":[34,69,127],"degradation":[35],"single":[37,156],"multiple":[39,64],"binary":[40,65,157,192],"is":[42,186],"unacceptable":[43],"for":[44,63,188],"modern":[45],"architectures":[46,174],"large":[48],"scale":[49],"datasets":[50],"like":[51,175],"ImageNet.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"proposed":[57,92,145],"a":[58,108],"Piecewise":[59],"Approximation":[60],"(PA)":[61],"scheme":[62,94,168],"lessens":[68],"loss":[70],"by":[71],"approximating":[72],"full":[73,98,131],"precision":[74,99,132],"weights":[75,100,161],"activations":[77,164],"efficiently,":[78],"maintains":[80],"parallelism":[81],"bitwise":[83],"to":[85,133,172],"guarantee":[86],"efficiency.":[87],"Unlike":[88],"previous":[89],"approaches,":[90],"PA":[93,167],"segments":[95],"piece-wisely":[96],"activations,":[102],"approximates":[104],"each":[105],"piece":[106],"with":[107,115,130,159,179],"scaling":[109],"coefficient.":[110],"Our":[111],"implementation":[112],"ResNet":[114,184],"different":[116],"depths":[117],"ImageNet":[119],"both":[122],"Top-1":[123],"Top-5":[125],"classification":[126],"gap":[128],"compared":[129],"approximately":[134],"1.0%.":[135],"Benefited":[136],"from":[137],"binarization":[139],"downsampling":[142],"layer,":[143],"our":[144],"PA-ResNet50":[146],"requires":[147],"less":[148],"usage":[150],"two":[152],"times":[153],"Flops":[154],"than":[155],"4":[160],"5":[163],"bases.":[165],"The":[166,194],"also":[170],"generalize":[171],"other":[173,189],"DenseNet":[176],"MobileNet":[178],"similar":[180],"approximation":[181],"power":[182],"as":[183],"promising":[187],"tasks":[190],"using":[191],"convolutions.":[193],"code":[195],"pretrained":[197],"models":[198],"will":[199],"be":[200],"publicly":[201],"available.</p>":[202]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-07T14:30:12.667765","created_date":"2025-02-22T00:00:00"}