{"id":"https://openalex.org/W3200737461","doi":"https://doi.org/10.1109/mlcad52597.2021.9531245","title":"Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores","display_name":"Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores","publication_year":2021,"publication_date":"2021-08-30","ids":{"openalex":"https://openalex.org/W3200737461","doi":"https://doi.org/10.1109/mlcad52597.2021.9531245","mag":"3200737461"},"language":"en","primary_location":{"id":"doi:10.1109/mlcad52597.2021.9531245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlcad52597.2021.9531245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","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/A5021503170","display_name":"Ahsan Saeed","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145457","display_name":"Robert Bosch (Taiwan)","ror":"https://ror.org/046as2g47","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210145457","https://openalex.org/I889804353"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE","TW"],"is_corresponding":false,"raw_author_name":"Ahsan Saeed","raw_affiliation_strings":["Robert Bosch GmbH","Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210145457"]},{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011419637","display_name":"Daniel Mueller-Gritschneder","orcid":"https://orcid.org/0000-0003-0903-631X"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Mueller-Gritschneder","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057115467","display_name":"Falk Rehm","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145457","display_name":"Robert Bosch (Taiwan)","ror":"https://ror.org/046as2g47","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210145457","https://openalex.org/I889804353"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Falk Rehm","raw_affiliation_strings":["Robert Bosch GmbH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210145457"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018259456","display_name":"Arne Hamann","orcid":"https://orcid.org/0000-0002-9016-3641"},"institutions":[{"id":"https://openalex.org/I4210145457","display_name":"Robert Bosch (Taiwan)","ror":"https://ror.org/046as2g47","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210145457","https://openalex.org/I889804353"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Arne Hamann","raw_affiliation_strings":["Robert Bosch GmbH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210145457"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081198825","display_name":"Dirk Ziegenbein","orcid":"https://orcid.org/0000-0002-8676-0048"},"institutions":[{"id":"https://openalex.org/I4210145457","display_name":"Robert Bosch (Taiwan)","ror":"https://ror.org/046as2g47","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210145457","https://openalex.org/I889804353"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Dirk Ziegenbein","raw_affiliation_strings":["Robert Bosch GmbH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210145457"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017567485","display_name":"Ulf Schlichtmann","orcid":"https://orcid.org/0000-0003-4431-7619"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ulf Schlichtmann","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046024226","display_name":"Andreas Gerstlauer","orcid":"https://orcid.org/0000-0002-6748-2054"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Gerstlauer","raw_affiliation_strings":["The University of Texas at Austin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Austin","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6809,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66784737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11181","display_name":"Advanced Data Storage Technologies","score":0.9987000226974487,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9983999729156494,"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/computer-science","display_name":"Computer science","score":0.8206473588943481},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7817713618278503},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.748981773853302},{"id":"https://openalex.org/keywords/execution-time","display_name":"Execution time","score":0.552589476108551},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5214996933937073},{"id":"https://openalex.org/keywords/performance-prediction","display_name":"Performance prediction","score":0.4922308027744293},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.45524826645851135},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.44719481468200684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4036591649055481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40310853719711304},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.329489529132843},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.22037586569786072},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.09072983264923096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8206473588943481},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7817713618278503},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.748981773853302},{"id":"https://openalex.org/C2989134064","wikidata":"https://www.wikidata.org/wiki/Q288510","display_name":"Execution time","level":2,"score":0.552589476108551},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5214996933937073},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.4922308027744293},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.45524826645851135},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.44719481468200684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036591649055481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40310853719711304},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.329489529132843},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.22037586569786072},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.09072983264923096},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlcad52597.2021.9531245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlcad52597.2021.9531245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","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":19,"referenced_works":["https://openalex.org/W2034062945","https://openalex.org/W2053834050","https://openalex.org/W2120635877","https://openalex.org/W2122825543","https://openalex.org/W2131230754","https://openalex.org/W2132511032","https://openalex.org/W2135046866","https://openalex.org/W2147657366","https://openalex.org/W2215983828","https://openalex.org/W2291445757","https://openalex.org/W2401063723","https://openalex.org/W2612052631","https://openalex.org/W2781844902","https://openalex.org/W2782784892","https://openalex.org/W2888297879","https://openalex.org/W2962995671","https://openalex.org/W2984960739","https://openalex.org/W6678353954","https://openalex.org/W6753910507"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W1592799665","https://openalex.org/W2048582679","https://openalex.org/W2782226720","https://openalex.org/W61261787","https://openalex.org/W2144011502","https://openalex.org/W3200737461","https://openalex.org/W2161309760"],"abstract_inverted_index":{"Early":[0],"run-time":[1,50,74,126,214],"prediction":[2,51,75,178,215],"of":[3,17,107,127,191],"co-running":[4,128,227],"independent":[5],"applications":[6,109,129],"prior":[7],"to":[8,43,66,91,95,123,151,207],"application":[9,36],"integration":[10],"becomes":[11],"challenging":[12],"in":[13,32,35,41,130,175],"multi-core":[14],"processors.":[15],"One":[16],"the":[18,23,26,111,118,125,145,152,189,195,200],"most":[19],"notable":[20],"causes":[21],"is":[22,121,136,171,205,218],"interference":[24,114,131,148,177],"at":[25],"main":[27],"memory":[28],"subsystem,":[29],"which":[30,170,217],"results":[31],"significant":[33],"degradation":[34],"performance":[37,97,102],"and":[38,58,64,100,113,147,210,223],"response":[39],"time":[40],"comparison":[42],"standalone":[44,112,146],"execution.":[45],"Currently":[46],"available":[47],"techniques":[48],"for":[49,73,80,104,225],"like":[52],"traditional":[53],"cycle-accurate":[54],"simulations":[55],"are":[56,61],"slow,":[57],"analytical":[59],"models":[60],"not":[62,78],"accurate":[63],"time-consuming":[65],"build.":[67],"By":[68],"contrast,":[69],"existing":[70],"machine-learning-based":[71],"approaches":[72],"simply":[76],"do":[77],"account":[79],"interference.":[81],"In":[82,133,184],"this":[83],"paper,":[84],"we":[85,161,186],"use":[86],"a":[87,93,105,163,172,180],"machine":[88,181],"learning-based":[89],"approach":[90,204],"train":[92],"model":[94,120,201],"correlate":[96],"data":[98],"(instructions":[99],"hardware":[101],"counters)":[103],"set":[106],"benchmark":[108],"between":[110,141],"scenarios.":[115,132],"After":[116],"that,":[117],"trained":[119],"used":[122],"predict":[124],"general,":[134],"there":[135],"no":[137],"straightforward":[138],"one-to-one":[139],"correspondence":[140],"samples":[142],"obtained":[143],"from":[144],"scenarios":[149],"due":[150],"different":[153],"run-times,":[154],"i.e.":[155],"execution":[156],"speeds.":[157],"To":[158],"address":[159],"this,":[160],"developed":[162],"simple":[164],"yet":[165],"effective":[166,209],"sample":[167],"alignment":[168],"algorithm,":[169],"key":[173],"component":[174],"transforming":[176],"into":[179],"learning":[182],"problem.":[183],"addition,":[185],"systematically":[187],"identify":[188],"subset":[190],"features":[192],"that":[193],"have":[194],"highest":[196],"positive":[197],"impact":[198],"on":[199],"performance.":[202],"Our":[203],"demonstrated":[206],"be":[208],"shows":[211],"an":[212],"average":[213],"error,":[216],"as":[219,221],"low":[220],"0.3%":[222],"0.1%":[224],"two":[226],"applications.":[228]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
