{"id":"https://openalex.org/W3159643370","doi":"https://doi.org/10.1109/iscas51556.2021.9401526","title":"High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator","display_name":"High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator","publication_year":2021,"publication_date":"2021-04-27","ids":{"openalex":"https://openalex.org/W3159643370","doi":"https://doi.org/10.1109/iscas51556.2021.9401526","mag":"3159643370"},"language":"en","primary_location":{"id":"doi:10.1109/iscas51556.2021.9401526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-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/A5101777755","display_name":"Kuan-Ting Lin","orcid":"https://orcid.org/0000-0003-1560-0917"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Kuan-Ting Lin","raw_affiliation_strings":["Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102810370","display_name":"Ching-Te Chiu","orcid":"https://orcid.org/0000-0002-8396-2883"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ching-Te Chiu","raw_affiliation_strings":["Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090557182","display_name":"Jheng-Yi Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jheng-Yi Chang","raw_affiliation_strings":["Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026677825","display_name":"Shan-Chien Hsiao","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shan-Chien Hsiao","raw_affiliation_strings":["Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101777755"],"corresponding_institution_ids":["https://openalex.org/I25846049"],"apc_list":null,"apc_paid":null,"fwci":0.7685,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72947712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9990000128746033,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9980999827384949,"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/computer-science","display_name":"Computer science","score":0.8556692600250244},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6962282657623291},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5684441328048706},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.5543785691261292},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5505110621452332},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.49518290162086487},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.491984099149704},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4887228012084961},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4752873182296753},{"id":"https://openalex.org/keywords/static-random-access-memory","display_name":"Static random-access memory","score":0.41093170642852783},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3658296465873718},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3566385507583618},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.32172220945358276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31509989500045776},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.25238171219825745},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.12558084726333618},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.11641481518745422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8556692600250244},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6962282657623291},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5684441328048706},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.5543785691261292},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5505110621452332},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.49518290162086487},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.491984099149704},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4887228012084961},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4752873182296753},{"id":"https://openalex.org/C68043766","wikidata":"https://www.wikidata.org/wiki/Q267416","display_name":"Static random-access memory","level":2,"score":0.41093170642852783},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3658296465873718},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3566385507583618},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.32172220945358276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31509989500045776},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.25238171219825745},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.12558084726333618},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.11641481518745422},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas51556.2021.9401526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2289252105","https://openalex.org/W2605487586","https://openalex.org/W2612445135","https://openalex.org/W2734572653","https://openalex.org/W2767644592","https://openalex.org/W2789333991","https://openalex.org/W2962835968","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2969985801","https://openalex.org/W2979042679","https://openalex.org/W2979590285","https://openalex.org/W2990185852","https://openalex.org/W3015913419","https://openalex.org/W3035693354","https://openalex.org/W3038347349","https://openalex.org/W3102659484","https://openalex.org/W4297775537","https://openalex.org/W6628973269","https://openalex.org/W6637373629","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W3046471834","https://openalex.org/W3176282186","https://openalex.org/W4319952061","https://openalex.org/W3082465740","https://openalex.org/W4387489555","https://openalex.org/W4390945455","https://openalex.org/W2973622361","https://openalex.org/W4280636456","https://openalex.org/W3109468088","https://openalex.org/W4293053895"],"abstract_inverted_index":{"Deep":[0],"convolution":[1,72],"Neural":[2],"Network":[3],"(DCNN)":[4],"has":[5,20,194],"been":[6],"widely":[7],"used":[8],"in":[9,111,143,176,186,258],"computer":[10],"vision":[11],"tasks.":[12],"However,":[13],"for":[14,43,88],"edge":[15],"device,":[16],"even":[17],"then":[18],"inference":[19,35,56],"too":[21],"large":[22],"computational":[23],"complexity":[24],"and":[25,114,129,156,251,266],"data":[26,142,152,206,227],"access":[27,228],"amount.":[28,229],"Due":[29],"to":[30,104,136,147,164,172,215,239],"the":[31,34,64,75,79,93,106,112,117,120,138,144,149,166,169,177,180,183,189,209,216,231,244,256],"mentioned":[32],"shortcomings,":[33],"latency":[36],"of":[37,78,95,108,119,141,151,168,182,205,226,243],"state-of-the-art":[38],"models":[39,90],"are":[40],"still":[41],"impractical":[42],"real-world":[44],"applications.":[45],"In":[46],"this":[47,187],"paper,":[48,188],"we":[49,68,83,99,124,159,221,234,260],"proposed":[50,184],"a":[51,202],"high":[52,196],"utilization":[53,198],"energy-aware":[54],"real-time":[55,241],"deep":[57],"convolutional":[58],"neural":[59],"network":[60],"accelerator,":[61],"which":[62],"outperforms":[63],"current":[65,109],"accelerators.":[66],"First,":[67],"use":[69,100,115],"1x1":[70],"size":[71],"kernels":[73],"as":[74,116],"smallest":[76],"unit":[77,87],"computing":[80,86,237],"unit.":[81],"And":[82],"design":[84,257],"suitable":[85],"different":[89],"based":[91],"on":[92,208],"requirement":[94],"each":[96],"model.":[97],"Second,":[98],"Reuse":[101,127],"Feature":[102],"SRAM":[103],"store":[105],"output":[107],"layer":[110,171],"chip":[113,145,193],"input":[118],"next":[121],"layer.":[122],"Moreover,":[123],"import":[125],"Output":[126],"Strategy":[128],"Ring":[130],"Stream":[131],"Data":[132],"flow":[133],"not":[134],"only":[135],"expand":[137],"reuse":[139,219],"rate":[140],"but":[146],"reduce":[148,201,223],"amount":[150,204],"exchange":[153],"between":[154],"chips":[155],"DRAM.":[157],"Finally,":[158],"present":[160],"On-fly":[161],"Pooling":[162,170],"Module":[163],"let":[165],"calculation":[167],"be":[173],"completed":[174],"directly":[175],"chip.":[178],"With":[179],"aid":[181],"method":[185],"implemented":[190],"CNN":[191],"acceleration":[192],"extreme":[195],"hardware":[197],"rate.":[199],"We":[200],"generous":[203],"transfer":[207],"specific":[210],"module,":[211],"ECNN":[212],"[1].":[213],"Compared":[214,254],"methods":[217],"without":[218],"strategy,":[220],"can":[222,261],"533":[224],"times":[225,265],"At":[230],"same":[232],"time,":[233],"have":[235,267],"enough":[236],"power":[238],"perform":[240],"execution":[242],"existing":[245],"image":[246],"classification":[247],"model,":[248],"VGG16":[249],"[2]":[250],"MobileNet":[252],"[3].":[253],"with":[255],"[4],":[259],"speed":[262],"up":[263],"7.52":[264],"1.92x":[268],"energy":[269],"efficiency.":[270]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
