{"id":"https://openalex.org/W4405487687","doi":"https://doi.org/10.1109/tvlsi.2024.3515113","title":"VCNPU: An Algorithm-Hardware Co-Optimized Framework for Accelerating Neural Video Compression","display_name":"VCNPU: An Algorithm-Hardware Co-Optimized Framework for Accelerating Neural Video Compression","publication_year":2024,"publication_date":"2024-12-17","ids":{"openalex":"https://openalex.org/W4405487687","doi":"https://doi.org/10.1109/tvlsi.2024.3515113"},"language":"en","primary_location":{"id":"doi:10.1109/tvlsi.2024.3515113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2024.3515113","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","raw_type":"journal-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/A5100347369","display_name":"Siyu Zhang","orcid":"https://orcid.org/0000-0001-5531-8655"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Siyu Zhang","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101895317","display_name":"Wendong Mao","orcid":"https://orcid.org/0000-0002-1061-7165"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wendong Mao","raw_affiliation_strings":["School of Integrated Circuits, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100696999","display_name":"Zhongfeng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongfeng Wang","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100347369"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22739315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"4","first_page":"1014","last_page":"1027"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9940999746322632,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9940999746322632,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9921000003814697,"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":0.9912999868392944,"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":0.7126067280769348},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.6018442511558533},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.447139173746109},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41800469160079956},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38853466510772705},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.37362203001976013},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33375829458236694},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25938278436660767},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.08557641506195068}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7126067280769348},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.6018442511558533},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.447139173746109},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41800469160079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38853466510772705},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.37362203001976013},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33375829458236694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25938278436660767},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.08557641506195068},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvlsi.2024.3515113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2024.3515113","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3346210598","display_name":null,"funder_award_id":"2023A007","funder_id":"https://openalex.org/F4320329836","funder_display_name":"Chuzhou Science and Technology Program"},{"id":"https://openalex.org/G6422954184","display_name":null,"funder_award_id":"2022YFB4400600","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320329836","display_name":"Chuzhou Science and Technology Program","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W2132729131","https://openalex.org/W2140199336","https://openalex.org/W2146395539","https://openalex.org/W2172654076","https://openalex.org/W2511458122","https://openalex.org/W2769654144","https://openalex.org/W2798838583","https://openalex.org/W2963122961","https://openalex.org/W2969260367","https://openalex.org/W2981609437","https://openalex.org/W3013525232","https://openalex.org/W3018065762","https://openalex.org/W3020741905","https://openalex.org/W3025341614","https://openalex.org/W3031546776","https://openalex.org/W3034802763","https://openalex.org/W3047237654","https://openalex.org/W3090600872","https://openalex.org/W3107066575","https://openalex.org/W3108139283","https://openalex.org/W3110286842","https://openalex.org/W3134012069","https://openalex.org/W3164019007","https://openalex.org/W3165464242","https://openalex.org/W3173272744","https://openalex.org/W3201428215","https://openalex.org/W3205777752","https://openalex.org/W3210597831","https://openalex.org/W4223425316","https://openalex.org/W4290098589","https://openalex.org/W4308083841","https://openalex.org/W4312774595","https://openalex.org/W4312955127","https://openalex.org/W4313316261","https://openalex.org/W4316660130","https://openalex.org/W4360770950","https://openalex.org/W4376455279","https://openalex.org/W4386065617","https://openalex.org/W6675207249","https://openalex.org/W6747766405","https://openalex.org/W6802036239","https://openalex.org/W6839976848"],"related_works":["https://openalex.org/W2391251536","https://openalex.org/W2362198218","https://openalex.org/W2019521278","https://openalex.org/W1984922432","https://openalex.org/W2375008505","https://openalex.org/W1982750869","https://openalex.org/W2115140794","https://openalex.org/W2085756966","https://openalex.org/W2612632602","https://openalex.org/W2321805087"],"abstract_inverted_index":{"Video":[0],"compression":[1,22,34,65,83,95,229],"is":[2,12,86,116,152,172,198,209],"essential":[3],"for":[4,14],"storing":[5],"and":[6,51,92,160,183,194,208,255],"transmitting":[7],"video":[8,21,64,82,94,228,269],"content.":[9],"Real-time":[10],"decoding":[11,206],"indispensable":[13],"delivering":[15],"a":[16,59,80,103,112,147,167],"seamless":[17],"user":[18],"experience.":[19],"Neural":[20],"(NVC)":[23],"integrates":[24],"traditional":[25],"coding":[26,224],"techniques":[27],"with":[28],"deep":[29],"learning,":[30],"resulting":[31],"in":[32,121,243,252,263],"impressive":[33],"efficiency.":[35],"However,":[36],"the":[37,77,122,129,139,144,163,189],"real-time":[38],"deployment":[39],"of":[40,131,162,192,204],"advanced":[41],"NVC":[42,61],"models":[43],"encounters":[44],"challenges":[45],"due":[46],"to":[47,88,118,154,174,200,267],"their":[48],"high":[49],"complexity":[50],"extensive":[52,181],"off-chip":[53,176],"memory":[54],"access.":[55],"This":[56],"article":[57],"presents":[58],"novel":[60],"accelerator,":[62],"called":[63],"neural":[66],"processing":[67],"unit":[68],"(VCNPU),":[69],"via":[70],"an":[71],"algorithm-hardware":[72],"co-design":[73],"framework.":[74],"First,":[75],"at":[76,143],"algorithmic":[78],"level,":[79,146],"reparameterizable":[81],"network":[84],"(RepVCN)":[85],"proposed":[87,117],"aggregate":[89],"multiscale":[90],"features":[91],"boost":[93],"quality.":[96],"RepVCN":[97,120,221],"can":[98],"be":[99],"equivalently":[100],"transformed":[101],"into":[102],"streamlined":[104],"structure":[105],"without":[106],"extra":[107],"computations":[108],"after":[109],"training.":[110],"Second,":[111],"mask-sharing":[113],"pruning":[114],"strategy":[115],"compress":[119],"fast":[123,158],"transform":[124],"domain.":[125],"It":[126],"effectively":[127],"prevents":[128],"destruction":[130],"sparse":[132,149,157],"patterns":[133],"caused":[134,179],"by":[135,180],"model":[136,140],"simplification,":[137],"maintaining":[138],"capacity.":[141],"Third,":[142],"hardware":[145],"reconfigurable":[148],"computing":[150],"module":[151],"designed":[153],"flexibly":[155],"support":[156],"convolutions":[159],"deconvolutions":[161],"compact":[164],"RepVCN.":[165],"Besides,":[166],"hybrid":[168],"layer":[169],"fusion":[170],"pipeline":[171],"advocated":[173],"reduce":[175],"data":[177],"communication":[178],"motion":[182],"residual":[184],"features.":[185],"Finally,":[186],"based":[187],"on":[188],"joint":[190],"optimization":[191],"computation":[193],"communication,":[195],"our":[196,220,232],"VCNPU":[197,233],"constructed":[199],"realize":[201],"adaptive":[202],"adjustments":[203],"various":[205],"qualities":[207],"implemented":[210],"under":[211],"TSMC":[212],"28-nm":[213],"CMOS":[214],"technology.":[215],"Extensive":[216],"experiments":[217],"demonstrate":[218],"that":[219],"provides":[222],"superior":[223],"quality":[225],"over":[226],"other":[227],"baselines.":[230],"Meanwhile,":[231],"achieves":[234],"<inline-formula":[235,245,256],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[236,246,257],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[237,247,258],"<tex-math":[238,248,259],"notation=\"LaTeX\">$6.7\\times":[239],"$":[240,250,261],"</tex-math></inline-formula>":[241,251,262],"improvements":[242],"throughput,":[244],"notation=\"LaTeX\">$2.9\\times":[249],"area":[253],"efficiency,":[254],"notation=\"LaTeX\">$4\\times":[260],"energy":[264],"efficiency":[265],"compared":[266],"prior":[268],"processors.":[270]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}