{"id":"https://openalex.org/W2993061918","doi":"https://doi.org/10.1109/les.2019.2957487","title":"WoMA: An Input-Based Learning Model to Predict Dynamic Workload of Embedded Applications","display_name":"WoMA: An Input-Based Learning Model to Predict Dynamic Workload of Embedded Applications","publication_year":2019,"publication_date":"2019-12-05","ids":{"openalex":"https://openalex.org/W2993061918","doi":"https://doi.org/10.1109/les.2019.2957487","mag":"2993061918"},"language":"en","primary_location":{"id":"doi:10.1109/les.2019.2957487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/les.2019.2957487","pdf_url":null,"source":{"id":"https://openalex.org/S22443479","display_name":"IEEE Embedded Systems Letters","issn_l":"1943-0663","issn":["1943-0663","1943-0671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Embedded Systems Letters","raw_type":"journal-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/A5033356396","display_name":"Dongning Ma","orcid":"https://orcid.org/0000-0002-1879-4406"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongning Ma","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Villanova University, Villanova, USA"],"raw_orcid":"https://orcid.org/0000-0002-1879-4406","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Villanova University, Villanova, USA","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016146461","display_name":"Xun Jiao","orcid":"https://orcid.org/0000-0003-4476-2501"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Jiao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Villanova University, Villanova, USA"],"raw_orcid":"https://orcid.org/0000-0003-4476-2501","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Villanova University, Villanova, USA","institution_ids":["https://openalex.org/I7863295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I7863295"],"apc_list":null,"apc_paid":null,"fwci":0.2335,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55906947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":"3","first_page":"74","last_page":"77"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.9998999834060669,"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/T12238","display_name":"Green IT and Sustainability","score":0.9998999834060669,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9987999796867371,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9959999918937683,"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":0.8989890813827515},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.8965181112289429},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6550918817520142},{"id":"https://openalex.org/keywords/frequency-scaling","display_name":"Frequency scaling","score":0.5925622582435608},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.43200886249542236},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4286554157733917},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.30334633588790894},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.11538055539131165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8989890813827515},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.8965181112289429},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6550918817520142},{"id":"https://openalex.org/C157742956","wikidata":"https://www.wikidata.org/wiki/Q3237776","display_name":"Frequency scaling","level":3,"score":0.5925622582435608},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.43200886249542236},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4286554157733917},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.30334633588790894},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.11538055539131165},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/les.2019.2957487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/les.2019.2957487","pdf_url":null,"source":{"id":"https://openalex.org/S22443479","display_name":"IEEE Embedded Systems Letters","issn_l":"1943-0663","issn":["1943-0663","1943-0671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Embedded Systems Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9200000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W32488798","https://openalex.org/W1686420892","https://openalex.org/W1997249557","https://openalex.org/W2020217519","https://openalex.org/W2099961254","https://openalex.org/W2116785402","https://openalex.org/W2137693329","https://openalex.org/W2140959087","https://openalex.org/W2407452468","https://openalex.org/W2552045999","https://openalex.org/W4232751114","https://openalex.org/W4240259771","https://openalex.org/W4285719527","https://openalex.org/W6601310229"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W2000785801","https://openalex.org/W986318368","https://openalex.org/W2378211422","https://openalex.org/W2384410913","https://openalex.org/W2352878646","https://openalex.org/W4388145910","https://openalex.org/W2004734601","https://openalex.org/W2130149817","https://openalex.org/W2990194547"],"abstract_inverted_index":{"Embedded":[0],"applications":[1,71,90],"often":[2],"have":[3],"real-time":[4],"requirements":[5],"and":[6,114,129],"thus":[7],"meeting":[8],"the":[9,36,50,85,99,160],"user-specified":[10,37],"deadline":[11],"are":[12,175],"essential":[13],"for":[14,141],"good":[15],"user":[16],"experience.":[17],"Frequency":[18],"(down)":[19],"scaling":[20,134],"is":[21,44,78,91,135],"a":[22,40,59,106,153],"widely":[23],"used":[24],"technique":[25],"to":[26,33,48,63,110,181],"improve":[27],"energy":[28],"efficiency":[29],"but":[30],"may":[31],"lead":[32],"violations":[34],"of":[35,45,69,87,162,172],"deadline.":[38],"Thus,":[39,177],"workload":[41,66,86,147],"prediction":[42],"model":[43,62],"critical":[46],"importance":[47],"guide":[49,182],"frequency":[51,133,183],"scaling.":[52],"In":[53],"this":[54],"letter,":[55],"we":[56,104,144,178],"propose":[57],"WoMA,":[58],"supervised":[60],"learning":[61],"predict":[64,159],"dynamic":[65],"(execution":[67],"cycles)":[68],"embedded":[70,89,119],"based":[72],"on":[73,80],"their":[74,95],"input":[75,96],"data.":[76],"WoMA":[77,112,157,173,180],"built":[79],"our":[81],"key":[82],"observation":[83],"that":[84,170],"some":[88],"primarily":[92],"determined":[93],"by":[94,189],"size.":[97],"Using":[98],"\u201cinput":[100],"size\u201d":[101],"as":[102],"features,":[103],"apply":[105],"logistic":[107],"regression":[108],"method":[109],"construct":[111],"trained":[113],"tested":[115],"using":[116],"five":[117,149],"popular":[118],"applications:":[120],"1)":[121],"adpcm;":[122],"2)":[123],"aes;":[124],"3)":[125],"dfsin;":[126],"4)":[127],"blowfish;":[128],"5)":[130],"gsm.":[131],"Since":[132],"typically":[136],"performed":[137],"at":[138],"discrete":[139],"levels,":[140],"each":[142],"application,":[143],"classify":[145],"its":[146,163],"into":[148],"distinct":[150],"classes.":[151],"For":[152],"given":[154],"test":[155],"input,":[156],"will":[158],"class":[161],"corresponding":[164],"workload.":[165],"Cycle-accurate":[166],"simulation":[167],"results":[168],"show":[169],"100%":[171],"predictions":[174],"accurate.":[176],"use":[179],"scaling,":[184],"resulting":[185],"in":[186],"power":[187],"saving":[188],"9.9%-61.8%":[190],"across":[191],"these":[192],"benchmark":[193],"applications.":[194]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}