{"id":"https://openalex.org/W4392849917","doi":"https://doi.org/10.1145/3652593","title":"REC: REtime Convolutional Layers to Fully Exploit Harvested Energy for ReRAM-based CNN Accelerators","display_name":"REC: REtime Convolutional Layers to Fully Exploit Harvested Energy for ReRAM-based CNN Accelerators","publication_year":2024,"publication_date":"2024-03-15","ids":{"openalex":"https://openalex.org/W4392849917","doi":"https://doi.org/10.1145/3652593"},"language":"en","primary_location":{"id":"doi:10.1145/3652593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652593","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3652593","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017699482","display_name":"Kunyu Zhou","orcid":"https://orcid.org/0000-0001-8782-9927"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kunyu Zhou","raw_affiliation_strings":["Capital Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026823951","display_name":"Keni Qiu","orcid":"https://orcid.org/0000-0002-5851-777X"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keni Qiu","raw_affiliation_strings":["Capital Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017699482"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":0.2088,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46279024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"23","issue":"6","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9991000294685364,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9991000294685364,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9961000084877014,"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":0.992900013923645,"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/exploit","display_name":"Exploit","score":0.8044185638427734},{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.7234538793563843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5384158492088318},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5130943059921265},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48525500297546387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2295800745487213},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.18502479791641235},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1497851312160492},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12659111618995667},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1238689124584198}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.8044185638427734},{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.7234538793563843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5384158492088318},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5130943059921265},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48525500297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2295800745487213},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.18502479791641235},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1497851312160492},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12659111618995667},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1238689124584198},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652593","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3652593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652593","source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392849917.pdf","grobid_xml":"https://content.openalex.org/works/W4392849917.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W1598995842","https://openalex.org/W1979081194","https://openalex.org/W1980601381","https://openalex.org/W1989660200","https://openalex.org/W1993163906","https://openalex.org/W2004652177","https://openalex.org/W2009053018","https://openalex.org/W2019083297","https://openalex.org/W2037547340","https://openalex.org/W2041259587","https://openalex.org/W2046672513","https://openalex.org/W2048266589","https://openalex.org/W2056348412","https://openalex.org/W2059833876","https://openalex.org/W2066952306","https://openalex.org/W2094756095","https://openalex.org/W2094860564","https://openalex.org/W2112796928","https://openalex.org/W2117175221","https://openalex.org/W2124532166","https://openalex.org/W2141546789","https://openalex.org/W2141901012","https://openalex.org/W2276486856","https://openalex.org/W2294282016","https://openalex.org/W2300242332","https://openalex.org/W2530427072","https://openalex.org/W2535649113","https://openalex.org/W2537962049","https://openalex.org/W2562493617","https://openalex.org/W2571410096","https://openalex.org/W2576422999","https://openalex.org/W2587845665","https://openalex.org/W2608111952","https://openalex.org/W2613989746","https://openalex.org/W2620300161","https://openalex.org/W2625230690","https://openalex.org/W2742536119","https://openalex.org/W2761242776","https://openalex.org/W2765313912","https://openalex.org/W2767737961","https://openalex.org/W2809171749","https://openalex.org/W2883786987","https://openalex.org/W2886561805","https://openalex.org/W2889527112","https://openalex.org/W2897303255","https://openalex.org/W2903793739","https://openalex.org/W2913431662","https://openalex.org/W2913974869","https://openalex.org/W2934889147","https://openalex.org/W2962903741","https://openalex.org/W2993415086","https://openalex.org/W2998577050","https://openalex.org/W3004857775","https://openalex.org/W3007196687","https://openalex.org/W3012002440","https://openalex.org/W3017282344","https://openalex.org/W3102223484","https://openalex.org/W3103148960","https://openalex.org/W3126583178","https://openalex.org/W3199508312","https://openalex.org/W3201177868","https://openalex.org/W4243519499","https://openalex.org/W4251155475","https://openalex.org/W4251871580","https://openalex.org/W4254672563","https://openalex.org/W4293024216","https://openalex.org/W4321380840","https://openalex.org/W4364361118"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620"],"abstract_inverted_index":{"As":[0],"the":[1,36,53,65,74,103,106,114,121,142,154,164,177,188,203,236,253,266,282,286,295,301],"Internet":[2],"of":[3,38,109,116,137,147,167,174,180,191,239,275,304],"Things":[4],"(IoTs)":[5],"increasingly":[6],"combines":[7],"AI":[8],"technology,":[9],"it":[10],"is":[11,56,208,226,232],"a":[12,68,127,212,223,257],"trend":[13],"to":[14,64,140,162,197,202,215,228,234,251,278,281],"deploy":[15],"neural":[16,87],"network":[17],"algorithms":[18],"at":[19,153],"edges":[20],"and":[21,40,45,49,113,144],"make":[22],"IoT":[23,32],"devices":[24,33],"more":[25],"intelligent":[26],"than":[27],"ever.":[28],"Moreover,":[29,222],"energy-harvesting":[30],"technology-based":[31],"have":[34,94,101],"shown":[35],"advantages":[37],"green":[39],"low-carbon":[41],"economy,":[42],"convenient":[43],"maintenance,":[44],"theoretically":[46],"infinite":[47],"lifetime,":[48],"so":[50,195],"on.":[51],"However,":[52,98],"harvested":[54,75,92,117],"energy":[55,93,145,148],"often":[57],"unstable,":[58],"resulting":[59],"in":[60],"low":[61],"performance":[62,143,273],"due":[63],"fact":[66],"that":[67,133,230,265,294],"fixed":[69],"load":[70],"cannot":[71],"sufficiently":[72],"utilize":[73,217],"energy.":[76],"To":[77],"address":[78],"this":[79,124],"problem,":[80],"recent":[81],"works":[82,100],"focusing":[83],"on":[84],"ReRAM-based":[85,150],"convolutional":[86,135,169,178,193],"networks":[88],"(CNN)":[89],"accelerators":[90],"under":[91],"proposed":[95,267],"hardware/software":[96],"optimizations.":[97],"those":[99],"overlooked":[102],"mismatch":[104],"between":[105],"power":[107,160,165,205,220,259],"requirement":[108],"different":[110,159,168,192,199],"CNN":[111,138,200],"layers":[112,136,179,194,201],"variation":[115],"power.":[118],"Motivated":[119],"by":[120,184],"above":[122],"observation,":[123],"article":[125],"proposes":[126],"novel":[128],"strategy,":[129],"called":[130],"REC":[131,157,186,210,231,244,268,287,296],",":[132],"retimes":[134,187],"inferences":[139,242,250],"improve":[141,235,300],"efficiency":[146],"harvesting":[149],"accelerators.":[151],"Specifically,":[152],"offline":[155],"stage,":[156],"defines":[158],"levels":[161],"fit":[163],"requirements":[166],"layers.":[170],"At":[171],"runtime,":[172],"instead":[173],"sequentially":[175],"executing":[176],"an":[181,246,271],"inference":[182],"one":[183],"one,":[185],"execution":[189],"timeframe":[190],"as":[196],"accommodate":[198],"changing":[204],"inputs.":[206,221],"What":[207],"more,":[209],"provides":[211,245],"parallel":[213],"strategy":[214,284],"fully":[216],"very":[218],"high":[219,258],"case":[224,290],"study":[225,291],"presented":[227],"show":[229,264,293],"effective":[233],"real-time":[237,308],"accomplishment":[238],"periodical":[240,305],"critical":[241,249,306],"because":[243],"opportunity":[247],"for":[248],"preempt":[252],"process":[254],"window":[255],"with":[256],"supply.":[260],"Our":[261],"experimental":[262],"results":[263,292],"scheme":[269,297],"achieves":[270],"average":[272],"improvement":[274],"6.1\u00d7":[276],"(up":[277],"16.5\u00d7)":[279],"compared":[280],"traditional":[283],"without":[285],"idea.":[288],"The":[289],"can":[298],"significantly":[299],"success":[302],"rate":[303],"inferences\u2019":[307],"accomplishment.":[309]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}