{"id":"https://openalex.org/W3027858719","doi":"https://doi.org/10.1142/s0218126621500225","title":"UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition","display_name":"UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition","publication_year":2020,"publication_date":"2020-05-19","ids":{"openalex":"https://openalex.org/W3027858719","doi":"https://doi.org/10.1142/s0218126621500225","mag":"3027858719"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126621500225","doi":"https://doi.org/10.1142/s0218126621500225","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126621500225","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5100719382","display_name":"Chen Yang","orcid":"https://orcid.org/0000-0002-8221-7670","observed_orcids":["https://orcid.org/0000-0002-8221-7670","https://orcid.org/0009-0003-4116-0991"]},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chen Yang","raw_affiliation_strings":["School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-8221-7670","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335726","display_name":"Jingyu Zhang","orcid":"https://orcid.org/0000-0002-0330-3205","observed_orcids":["https://orcid.org/0000-0002-0330-3205"]},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Zhang","raw_affiliation_strings":["School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100340152","display_name":"Qi Chen","orcid":"https://orcid.org/0000-0002-1057-1099","observed_orcids":["https://orcid.org/0000-0002-1057-1099","https://orcid.org/0009-0006-0266-7927"]},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Chen","raw_affiliation_strings":["School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049, P. R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018343158","display_name":"Yi Xu","orcid":"https://orcid.org/0000-0003-2126-6054","observed_orcids":["https://orcid.org/0000-0003-2126-6054","https://orcid.org/0000-0003-4429-5982","https://orcid.org/0000-0002-0503-183X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Xu","raw_affiliation_strings":["Hangzhou Flashbillion Semiconductor Co. Ltd, Building 17, No. 57, Science and Technology Park Road, Baiyang District, Hangzhou, Zhejiang 310018, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Flashbillion Semiconductor Co. Ltd, Building 17, No. 57, Science and Technology Park Road, Baiyang District, Hangzhou, Zhejiang 310018, P. R. China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102017059","display_name":"Cimang Lu","orcid":"https://orcid.org/0000-0001-5515-032X","observed_orcids":["https://orcid.org/0000-0001-5515-032X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cimang Lu","raw_affiliation_strings":["Hangzhou Flashbillion Semiconductor Co. Ltd, Building 17, No. 57, Science and Technology Park Road, Baiyang District, Hangzhou, Zhejiang 310018, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Flashbillion Semiconductor Co. Ltd, Building 17, No. 57, Science and Technology Park Road, Baiyang District, Hangzhou, Zhejiang 310018, P. R. China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100719382"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.336,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.52961962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"30","issue":"02","first_page":"2150022","last_page":"2150022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9735999703407288,"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":0.9735999703407288,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.023600000888109207,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/in-memory-computing","display_name":"in-memory computing","score":0.7279999852180481},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"pedestrian detection","score":0.9350000023841858},{"id":"https://openalex.org/keywords/parameter-reduction","display_name":"parameter reduction","score":0.9020000100135803},{"id":"https://openalex.org/keywords/memory-bottleneck","display_name":"memory bottleneck","score":0.8389999866485596},{"id":"https://openalex.org/keywords/energy-efficiency","display_name":"energy efficiency","score":0.7860000133514404},{"id":"https://openalex.org/keywords/on-chip-memory","display_name":"on-chip memory","score":0.6790000200271606}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8604001998901367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8214429616928101},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6537631750106812},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.620529294013977},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.517537534236908},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4965820908546448},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46963736414909363},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4362448453903198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4268718957901001},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.42631202936172485},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.34133630990982056},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.34109729528427124},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09178322553634644},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126621500225","doi":"https://doi.org/10.1142/s0218126621500225","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126621500225","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3896154561","display_name":null,"funder_award_id":"61704136","funder_id":"https://openalex.org/F4320335581","funder_display_name":"Young Scientists Fund"},{"id":"https://openalex.org/G4613175821","display_name":null,"funder_award_id":"2017BSHEDZZ29","funder_id":"https://openalex.org/F4320326174","funder_display_name":"Shaanxi Province Postdoctoral Science Foundation"},{"id":"https://openalex.org/G830242832","display_name":null,"funder_award_id":"2018M631163","funder_id":"https://openalex.org/F4320335443","funder_display_name":"Postdoctoral Research Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320326174","display_name":"Shaanxi Province Postdoctoral Science Foundation","ror":null},{"id":"https://openalex.org/F4320335443","display_name":"Postdoctoral Research Foundation of China","ror":null},{"id":"https://openalex.org/F4320335581","display_name":"Young Scientists Fund","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2066877279","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2202715214","https://openalex.org/W2289252105","https://openalex.org/W2300242332","https://openalex.org/W2518281301","https://openalex.org/W2605487586","https://openalex.org/W2613989746","https://openalex.org/W2741948058","https://openalex.org/W2752782242","https://openalex.org/W2768900401","https://openalex.org/W2783681199","https://openalex.org/W2792893539","https://openalex.org/W2799725986","https://openalex.org/W2805638497","https://openalex.org/W2884227506","https://openalex.org/W2885077862","https://openalex.org/W2889354425","https://openalex.org/W2904317004","https://openalex.org/W2920081941","https://openalex.org/W3206507930"],"related_works":["https://openalex.org/W4408847242","https://openalex.org/W2518858372","https://openalex.org/W3005814217","https://openalex.org/W4396918254","https://openalex.org/W4389709174","https://openalex.org/W4225323978","https://openalex.org/W3013930185","https://openalex.org/W3173718057","https://openalex.org/W2783156638","https://openalex.org/W3173967513"],"abstract_inverted_index":{"Pedestrian":[0],"recognition":[1,207],"has":[2],"achieved":[3],"the":[4,9,71,115,126,129,134,146,157,169,173,177,181,186],"state-of-the-art":[5],"performance":[6],"due":[7],"to":[8,24,58,100,120,123,191,204],"progress":[10],"of":[11,51,62,108,128,136,143,172,179],"recent":[12],"convolutional":[13,76,89],"neural":[14,77,90],"network":[15,78,91],"(CNN).":[16],"However,":[17],"mainstream":[18],"CNN":[19,63],"models":[20],"are":[21,139],"too":[22],"complicated":[23],"emerging":[25],"Computing-In-Memory":[26],"(CIM)":[27],"architectures":[28],"for":[29,102,208],"hardware":[30],"implementation,":[31],"because":[32],"enormous":[33],"parameters":[34,61,138],"and":[35,99,132,160],"massive":[36],"intermediate":[37],"processing":[38],"results":[39],"may":[40],"incur":[41],"severe":[42],"\u201cmemory":[43],"bottleneck\u201d.":[44],"This":[45],"paper":[46],"proposed":[47],"a":[48,86,94],"design":[49],"methodology":[50],"Parameter":[52],"Substitution":[53],"with":[54],"Nodes":[55],"Compensation":[56],"(PSNC)":[57],"significantly":[59],"reduce":[60],"model":[64,84],"without":[65],"inference":[66,116],"accuracy":[67,117],"degradation.":[68],"Based":[69],"on":[70,111,125],"PSNC":[72],"methodology,":[73],"an":[74],"ultra-lightweight":[75],"(UL-CNN)":[79],"was":[80],"designed.":[81],"The":[82,105],"UL-CNN":[83,110],"is":[85,118],"specially":[87],"optimized":[88],"aiming":[92],"at":[93,145],"flash-based":[95],"CIM":[96],"architecture":[97,183],"(Conv-Flash)":[98],"apply":[101],"recognizing":[103],"person.":[104],"implementation":[106],"result":[107],"running":[109],"Conv-Flash":[112,182],"shows":[113],"that":[114],"up":[119,156],"94.7%.":[121],"Compared":[122],"LeNet-5,":[124],"premise":[127],"similar":[130],"operations":[131],"accuracy,":[133],"amounts":[135],"UL-CNN\u2019s":[137],"less":[140],"than":[141],"37%":[142],"LeNet-5":[144],"same":[147],"dataset":[148],"benchmark.":[149],"Such":[150],"parameter":[151],"reduction":[152],"can":[153,184],"dramatically":[154],"speed":[155],"training":[158],"process":[159],"economize":[161],"on-chip":[162],"storage":[163],"overhead,":[164],"as":[165,167],"well":[166],"save":[168],"power":[170],"consumption":[171],"memory":[174],"access.":[175],"With":[176],"aid":[178],"UL-CNN,":[180],"provide":[185],"best":[187],"energy":[188],"efficiency":[189],"compared":[190],"other":[192],"platforms":[193],"(CPU,":[194],"GPU,":[195],"FPGA,":[196],"etc.),":[197],"which":[198],"consumes":[199],"only":[200],"2.2":[201],"\u00d7":[202],"105J":[203],"complete":[205],"pedestrian":[206],"one":[209],"frame.":[210]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2020-05-29T00:00:00"}
