{"id":"https://openalex.org/W7130551559","doi":"https://doi.org/10.1109/apccas67402.2025.11377370","title":"ReNoC-ML: Reliability-Aware Network-on-Chip Performance Modeling Using Machine Learning","display_name":"ReNoC-ML: Reliability-Aware Network-on-Chip Performance Modeling Using Machine Learning","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W7130551559","doi":"https://doi.org/10.1109/apccas67402.2025.11377370"},"language":null,"primary_location":{"id":"doi:10.1109/apccas67402.2025.11377370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas67402.2025.11377370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","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/A5026532546","display_name":"Zhuofan Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I130343225","display_name":"Universiti Putra Malaysia","ror":"https://ror.org/02e91jd64","country_code":"MY","type":"education","lineage":["https://openalex.org/I130343225"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Zhuofan Lin","raw_affiliation_strings":["Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia"],"affiliations":[{"raw_affiliation_string":"Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia","institution_ids":["https://openalex.org/I130343225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024183398","display_name":"Yang Wei Lim","orcid":"https://orcid.org/0000-0001-8949-5203"},"institutions":[{"id":"https://openalex.org/I130343225","display_name":"Universiti Putra Malaysia","ror":"https://ror.org/02e91jd64","country_code":"MY","type":"education","lineage":["https://openalex.org/I130343225"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Yang Wei Lim","raw_affiliation_strings":["Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia"],"affiliations":[{"raw_affiliation_string":"Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia","institution_ids":["https://openalex.org/I130343225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126402263","display_name":"Yongfu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfu Li","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":null,"display_name":"Fakhrul Zaman Rokhani","orcid":null},"institutions":[{"id":"https://openalex.org/I130343225","display_name":"Universiti Putra Malaysia","ror":"https://ror.org/02e91jd64","country_code":"MY","type":"education","lineage":["https://openalex.org/I130343225"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Fakhrul Zaman Rokhani","raw_affiliation_strings":["Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia"],"affiliations":[{"raw_affiliation_string":"Universiti Putra Malaysia,Faculty of Engineering,Dept. of Comp. and Comm. Sys. Engineering,Selangor,Malaysia","institution_ids":["https://openalex.org/I130343225"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026532546"],"corresponding_institution_ids":["https://openalex.org/I130343225"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7608711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.00839999970048666,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.0013000000035390258,"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/speedup","display_name":"Speedup","score":0.6330000162124634},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5565000176429749},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5410000085830688},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5217999815940857},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49709999561309814},{"id":"https://openalex.org/keywords/interconnection","display_name":"Interconnection","score":0.4814000129699707},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.45170000195503235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7200999855995178},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6010000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5945000052452087},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5410000085830688},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C123745756","wikidata":"https://www.wikidata.org/wiki/Q1665949","display_name":"Interconnection","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.45170000195503235},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27379998564720154},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2728999853134155}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apccas67402.2025.11377370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas67402.2025.11377370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1993298594","https://openalex.org/W2071292513","https://openalex.org/W2118231264","https://openalex.org/W2314694109","https://openalex.org/W2901431755","https://openalex.org/W3005573029","https://openalex.org/W3090233962","https://openalex.org/W3208980915","https://openalex.org/W4293159304","https://openalex.org/W4385834426"],"related_works":[],"abstract_inverted_index":{"Network-on-Chip":[0],"(NoC)":[1],"architectures":[2],"have":[3],"emerged":[4],"as":[5],"a":[6,29,38,45,76,116,126],"promising":[7],"solution":[8],"to":[9,15,28,50,74,134],"address":[10],"the":[11,21,52,135],"interconnection":[12],"challenges":[13],"inherent":[14],"complex":[16],"multi-processor":[17],"integrated":[18],"circuits.":[19],"Nevertheless,":[20],"increased":[22],"network":[23,56],"size":[24],"of":[25,54,64,114,122,128],"NoC":[26,55,70],"leads":[27],"time-consuming":[30],"process":[31],"for":[32,95],"design":[33],"space":[34],"exploration":[35],"(DSE)":[36],"using":[37],"traditional":[39],"cycle-accurate":[40,69,136],"simulator.":[41,137],"This":[42],"study":[43],"proposed":[44],"machine":[46],"learning":[47],"(ML)":[48],"approach":[49],"accelerate":[51],"prediction":[53],"performance":[57],"(i.e.":[58],"average":[59],"delay)":[60],"while":[61],"incorporating":[62],"considerations":[63],"reliability-related":[65],"parameters":[66],"(ReNoC-ML).":[67],"A":[68],"simulator":[71],"was":[72],"employed":[73],"generate":[75],"comprehensive":[77],"dataset.":[78],"Following":[79],"preprocessing,":[80],"three":[81],"ML":[82],"algorithms,":[83],"namely":[84],"Random":[85],"Forest":[86],"(RF),":[87],"XGBoost,":[88],"and":[89,99,125],"Multilayer":[90],"Perceptron":[91],"(MLP)":[92],"were":[93],"assessed":[94],"their":[96],"predictive":[97],"accuracy":[98],"computational":[100],"efficiency.":[101],"The":[102],"XGBoost":[103],"model":[104],"exhibited":[105],"superior":[106],"performance,":[107],"attaining":[108],"an":[109],"<tex":[110],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[111,130],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{R}^{2}$</tex>":[112],"score":[113],"0.95,":[115],"Mean":[117],"Absolute":[118],"Percentage":[119],"error":[120],"(MAPE)":[121],"9.54":[123],"%,":[124],"speedup":[127],"10<sup":[129],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">7</sup>":[131],"in":[132],"comparison":[133]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-02-20T00:00:00"}