{"id":"https://openalex.org/W4414693538","doi":"https://doi.org/10.1109/tgrs.2025.3616011","title":"High-Throughput Energy-Efficient Accelerator With Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing","display_name":"High-Throughput Energy-Efficient Accelerator With Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414693538","doi":"https://doi.org/10.1109/tgrs.2025.3616011"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2025.3616011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3616011","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","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":null,"display_name":"Tong Wang","orcid":"https://orcid.org/0009-0002-7557-1978"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tong Wang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327685","display_name":"He Chen","orcid":"https://orcid.org/0000-0003-4182-6493"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Chen","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006303328","display_name":"Ning Zhang","orcid":"https://orcid.org/0000-0003-4717-2304"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101299856","display_name":"Shuo Ni","orcid":"https://orcid.org/0009-0004-8155-8221"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Ni","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430806","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0001-7331-3292"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I50632499","display_name":"Chongqing University of Technology","ror":"https://ror.org/04vgbd477","country_code":"CN","type":"education","lineage":["https://openalex.org/I50632499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Zhang","raw_affiliation_strings":["Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China","institution_ids":["https://openalex.org/I50632499","https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334661","display_name":"Liang Chen","orcid":"https://orcid.org/0000-0003-3273-5522"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Chen","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":2.092,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88670038,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9049000144004822,"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"}},"topics":[{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9049000144004822,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9035000205039978,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5813999772071838},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5063999891281128},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.43459999561309814},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4207000136375427},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.39089998602867126},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.383899986743927},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.3693000078201294},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.36149999499320984},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3614000082015991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8424999713897705},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5813999772071838},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5699999928474426},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.43459999561309814},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4343000054359436},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4207000136375427},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3684999942779541},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3630000054836273},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.36149999499320984},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C167740415","wikidata":"https://www.wikidata.org/wiki/Q2876213","display_name":"Aerospace","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C168781493","wikidata":"https://www.wikidata.org/wiki/Q80585","display_name":"Associative array","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27399998903274536},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3616011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3616011","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1794565289","display_name":null,"funder_award_id":"2022YFB3902304","funder_id":"https://openalex.org/F4320323110","funder_display_name":"Beijing Institute of Technology"}],"funders":[{"id":"https://openalex.org/F4320323110","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W2935524202","https://openalex.org/W2954048742","https://openalex.org/W2962851801","https://openalex.org/W2964233199","https://openalex.org/W3014641619","https://openalex.org/W3033960544","https://openalex.org/W3082058622","https://openalex.org/W3100837220","https://openalex.org/W3129147932","https://openalex.org/W3132597740","https://openalex.org/W3132745255","https://openalex.org/W3136450870","https://openalex.org/W3137147200","https://openalex.org/W3164217046","https://openalex.org/W3173648655","https://openalex.org/W3200722869","https://openalex.org/W3205807913","https://openalex.org/W3217488171","https://openalex.org/W4200630931","https://openalex.org/W4210692941","https://openalex.org/W4214620071","https://openalex.org/W4214870144","https://openalex.org/W4220890517","https://openalex.org/W4283722322","https://openalex.org/W4285590283","https://openalex.org/W4312037452","https://openalex.org/W4312454178","https://openalex.org/W4320712936","https://openalex.org/W4321488410","https://openalex.org/W4361855179","https://openalex.org/W4377001513","https://openalex.org/W4386071870","https://openalex.org/W4386083031","https://openalex.org/W4386175066","https://openalex.org/W4386275701","https://openalex.org/W4386362974","https://openalex.org/W4389722504","https://openalex.org/W4390771525","https://openalex.org/W4390871702","https://openalex.org/W4390956162","https://openalex.org/W4392904289","https://openalex.org/W4394585936","https://openalex.org/W4401726555","https://openalex.org/W4402835564","https://openalex.org/W4403511199","https://openalex.org/W4403598898","https://openalex.org/W4404411459","https://openalex.org/W4407736406","https://openalex.org/W4407937864","https://openalex.org/W4409155583","https://openalex.org/W4409426948"],"related_works":[],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"achieved":[5],"remarkable":[6],"breakthroughs":[7],"on":[8,60,154],"remote":[9,20,43],"sensing":[10,21,44],"tasks":[11,161],"in":[12,37,133],"recent":[13],"years.":[14],"However,":[15],"deploying":[16],"CNNs":[17],"for":[18,88,142],"real-time":[19,32,42],"on-board":[22],"processing":[23],"still":[24],"remains":[25],"a":[26,40,65,112,137],"challenge":[27],"due":[28],"to":[29,55,72,127],"power":[30],"consumption,":[31],"and":[33,50,104,144,158,164,184],"other":[34],"limitations.":[35],"Therefore,":[36],"this":[38],"article,":[39],"satellite-based":[41],"accelerator":[45],"is":[46,70,86,117,125,140],"proposed,":[47],"where":[48,100],"algorithm":[49],"hardware":[51],"approaches":[52],"are":[53,98,106,152],"proposed":[54,71,126,172,186,202],"jointly":[56],"optimize":[57],"CNNs\u2019":[58],"deployment":[59],"edge-side":[61],"aerospace":[62],"devices.":[63],"Firstly,":[64],"collaborative-trainable":[66],"sparse-quantization":[67],"(CTSQ)":[68],"method":[69,174],"reduce":[73],"the":[74,79,84,89,93,101,171,176,185,190,198,201],"model\u2019s":[75],"storage":[76],"overhead.":[77],"In":[78],"CTSQ":[80,173],"method,":[81],"analysis":[82],"of":[83,179,192,200],"errors":[85],"performed":[87,107],"sparsity-quantization":[90],"composition.":[91],"Besides,":[92],"inter-channel":[94],"correlations":[95],"among":[96],"parameters":[97],"leveraged,":[99],"structured":[102],"sparsity":[103],"quantization":[105],"with":[108,162],"fine-grained":[109],"units.":[110],"Secondly,":[111],"modular-system":[113],"co-optimized":[114],"(MoSyC)":[115],"architecture":[116,139,188],"proposed.":[118],"A":[119],"hardware-mapped":[120],"sparse":[121,134],"access":[122],"(HMSA)":[123],"strategy":[124],"effectively":[128],"filter":[129],"out":[130],"zero":[131],"elements":[132],"parameters.":[135],"Moreover,":[136],"high-throughput":[138],"designed":[141],"parallel":[143],"pipelined":[145],"data":[146],"flow":[147],"control.":[148],"Finally,":[149],"extensive":[150],"experiments":[151],"conducted":[153],"both":[155],"scene":[156],"classification":[157],"object":[159],"detection":[160],"ResNet":[163],"YOLOv5":[165],"models.":[166],"The":[167],"results":[168],"show":[169],"that":[170],"achieves":[175,189],"compression":[177],"ratio":[178],"more":[180,193],"than":[181,194],"13.81":[182],"times,":[183],"MoSyC":[187],"throughput":[191],"1815":[195],"GOPS,":[196],"demonstrating":[197],"effectiveness":[199],"accelerator.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}