{"id":"https://openalex.org/W4416963480","doi":"https://doi.org/10.1109/islped65674.2025.11261700","title":"LoRASensE: Learnable Low-Rank Acquisition in Sensors for Efficient Edge Machine Vision","display_name":"LoRASensE: Learnable Low-Rank Acquisition in Sensors for Efficient Edge Machine Vision","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W4416963480","doi":"https://doi.org/10.1109/islped65674.2025.11261700"},"language":null,"primary_location":{"id":"doi:10.1109/islped65674.2025.11261700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5006343148","display_name":"Yiwen Liang","orcid":"https://orcid.org/0000-0002-8297-5009"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiwen Liang","raw_affiliation_strings":["The George Washington University,Washington, D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Washington, D.C.,USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108326780","display_name":"Z. Y. Yi","orcid":"https://orcid.org/0009-0001-7288-498X"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqiang Yi","raw_affiliation_strings":["The George Washington University,Washington, D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Washington, D.C.,USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075420600","display_name":"Tianrui Ma","orcid":"https://orcid.org/0000-0003-0894-6653"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Ma","raw_affiliation_strings":["Institute of Computing Technology of CAS,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computing Technology of CAS,Beijing,China","institution_ids":["https://openalex.org/I4210090176"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062801966","display_name":"Weidong Cao","orcid":"https://orcid.org/0000-0001-7539-8250"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weidong Cao","raw_affiliation_strings":["The George Washington University,Washington, D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Washington, D.C.,USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.7077000141143799,"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":0.7077000141143799,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09889999777078629,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.03449999913573265,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/machine-vision","display_name":"Machine vision","score":0.6284000277519226},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5616000294685364},{"id":"https://openalex.org/keywords/data-acquisition","display_name":"Data acquisition","score":0.5299999713897705},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.47369998693466187},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.4207000136375427},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.4000000059604645},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3849000036716461},{"id":"https://openalex.org/keywords/image-sensor","display_name":"Image sensor","score":0.383899986743927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7264999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.65829998254776},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.6284000277519226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6201000213623047},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.47369998693466187},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4207000136375427},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.4000000059604645},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C76935873","wikidata":"https://www.wikidata.org/wiki/Q209121","display_name":"Image sensor","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C131097465","wikidata":"https://www.wikidata.org/wiki/Q178898","display_name":"Gas compressor","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C193611912","wikidata":"https://www.wikidata.org/wiki/Q4677596","display_name":"Active vision","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped65674.2025.11261700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1996901117","https://openalex.org/W2040903332","https://openalex.org/W2060709554","https://openalex.org/W2104636679","https://openalex.org/W2118216984","https://openalex.org/W2122548617","https://openalex.org/W2132159895","https://openalex.org/W2138674095","https://openalex.org/W2194775991","https://openalex.org/W2197399834","https://openalex.org/W2285660444","https://openalex.org/W2339611261","https://openalex.org/W2511743527","https://openalex.org/W2593882908","https://openalex.org/W2774669597","https://openalex.org/W2798309714","https://openalex.org/W2899519551","https://openalex.org/W2945146780","https://openalex.org/W2963037989","https://openalex.org/W2964057022","https://openalex.org/W2996765167","https://openalex.org/W3036519573","https://openalex.org/W3047135694","https://openalex.org/W3133725905","https://openalex.org/W3133854994","https://openalex.org/W3134748538","https://openalex.org/W3135062777","https://openalex.org/W3161891823","https://openalex.org/W3174208823","https://openalex.org/W3190062760","https://openalex.org/W4221049191","https://openalex.org/W4285676450","https://openalex.org/W4380881065","https://openalex.org/W4414198535"],"related_works":[],"abstract_inverted_index":{"Integrating":[0],"deep":[1],"learning":[2,59],"with":[3,56,147,171],"ubiquitous":[4],"image":[5,83,125],"sensors":[6,75,104,126],"has":[7],"empowered":[8],"various":[9,159],"edge":[10,37,109],"vision":[11,110,138,160],"applications":[12,22,161],"such":[13],"as":[14],"classification,":[15],"segmentation,":[16],"and":[17,63,85,144,179],"detection.":[18],"Deploying":[19],"these":[20],"data-driven":[21,108],"requires":[23],"holistic":[24],"optimizations,":[25],"from":[26],"front-end":[27,74],"sensing":[28],"to":[29,80,127,140,184],"back-end":[30],"processing,":[31],"within":[32],"the":[33,46,53,70,86,185],"limited":[34,78],"resources":[35],"of":[36,49,58,65,73],"devices.":[38],"While":[39],"significant":[40],"advances":[41],"have":[42],"been":[43],"made":[44],"in":[45,52,103],"efficient":[47,107,148],"processing":[48,150],"sensory":[50],"data":[51,90],"back":[54],"end":[55],"optimizations":[57],"algorithms":[60],"(e.g.,":[61,68],"compression)":[62],"development":[64],"computing":[66],"hardware":[67],"accelerators),":[69],"energy":[71,181],"efficiency":[72],"remains":[76],"significantly":[77,196],"due":[79],"conventional":[81,186],"high-fidelity":[82,187],"acquisition":[84,97,102],"resulting":[87],"massive":[88],"off-chip":[89],"transfer.This":[91],"paper":[92],"proposes":[93],"a":[94,120,167,172],"domain-specific":[95],"visual":[96],"method,":[98],"LoRASensE,":[99],"learnable":[100],"low-rank":[101,122],"tailored":[105],"for":[106],"applications.":[111],"LoRASensE":[112,164,194],"is":[113,134,145],"an":[114],"algorithm-hardware":[115],"co-design":[116],"framework":[117],"that":[118,163,193],"integrates":[119],"learned":[121],"compressor":[123,133],"into":[124],"acquire":[128],"compressed":[129],"features.":[130],"Specifically,":[131],"this":[132],"optimized":[135],"alongside":[136],"downstream":[137],"tasks":[139],"ensure":[141],"end-to-end":[142],"accuracy":[143,177],"implemented":[146],"analog":[149],"hardware.":[151],"Our":[152],"extensive":[153],"evaluations":[154],"on":[155],"real-world":[156],"datasets":[157],"across":[158],"demonstrate":[162],"can":[165],"achieve":[166],"12.5\u00d7":[168],"compression":[169,200],"ratio":[170],"just":[173],"1-b":[174],"compressor,":[175],"minimal":[176],"loss,":[178],"86.9%":[180],"saving":[182],"compared":[183],"acquisition.":[188],"Multi-dimensional":[189],"comparisons":[190],"further":[191],"show":[192],"also":[195],"outperforms":[197],"existing":[198],"in-sensor":[199],"methods.":[201]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-03T00:00:00"}