{"id":"https://openalex.org/W4416963789","doi":"https://doi.org/10.1109/islped65674.2025.11261702","title":"ML-Power: Machine Learning based Power Estimation for SoCs","display_name":"ML-Power: Machine Learning based Power Estimation for SoCs","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W4416963789","doi":"https://doi.org/10.1109/islped65674.2025.11261702"},"language":null,"primary_location":{"id":"doi:10.1109/islped65674.2025.11261702","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Sujay Pandit","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sujay Pandit","raw_affiliation_strings":["Purdue University,School of ECE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University,School of ECE","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105369696","display_name":"Sujit Dey","orcid":"https://orcid.org/0000-0001-9671-3950"},"institutions":[{"id":"https://openalex.org/I2800935791","display_name":"UC San Diego Health System","ror":"https://ror.org/01kbfgm16","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2800935791"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sujit Dey","raw_affiliation_strings":["UC San Diego,Department of ECE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC San Diego,Department of ECE","institution_ids":["https://openalex.org/I2800935791"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065766721","display_name":"Anand Raghunathan","orcid":"https://orcid.org/0000-0002-4624-564X"},"institutions":[{"id":"https://openalex.org/I2800935791","display_name":"UC San Diego Health System","ror":"https://ror.org/01kbfgm16","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2800935791"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Raghunathan","raw_affiliation_strings":["UC San Diego,Department of ECE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC San Diego,Department of ECE","institution_ids":["https://openalex.org/I2800935791"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.7311999797821045,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.7311999797821045,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.14810000360012054,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.05779999867081642,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.6165000200271606},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.6025000214576721},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5038999915122986},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.4499000012874603},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4050999879837036},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.384799987077713},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.36959999799728394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6412000060081482},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.6165000200271606},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4235000014305115},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38850000500679016},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2777526511","wikidata":"https://www.wikidata.org/wiki/Q691543","display_name":"Pace","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C168292644","wikidata":"https://www.wikidata.org/wiki/Q10860336","display_name":"Power optimization","level":4,"score":0.34529998898506165},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3061000108718872},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.29750001430511475},{"id":"https://openalex.org/C118993495","wikidata":"https://www.wikidata.org/wiki/Q5042828","display_name":"Electrical efficiency","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2624000012874603},{"id":"https://openalex.org/C45872418","wikidata":"https://www.wikidata.org/wiki/Q5318966","display_name":"Dynamic demand","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped65674.2025.11261702","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320306087","display_name":"Semiconductor Research Corporation","ror":"https://ror.org/047z4n946"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"},{"id":"https://openalex.org/F4320337480","display_name":"Basic Energy Sciences","ror":"https://ror.org/05mg91w61"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1616044875","https://openalex.org/W1965125844","https://openalex.org/W2002072104","https://openalex.org/W2027829345","https://openalex.org/W2028800864","https://openalex.org/W2053484893","https://openalex.org/W2104773674","https://openalex.org/W2111589882","https://openalex.org/W2120000030","https://openalex.org/W2124523382","https://openalex.org/W2143727503","https://openalex.org/W2294097286","https://openalex.org/W2963309363","https://openalex.org/W2964199361","https://openalex.org/W2979854933","https://openalex.org/W3042760681","https://openalex.org/W3092027164","https://openalex.org/W3145579537","https://openalex.org/W3206406968","https://openalex.org/W3213528054","https://openalex.org/W4223929908","https://openalex.org/W4229807745","https://openalex.org/W4240199505","https://openalex.org/W4244593655","https://openalex.org/W4248651289","https://openalex.org/W4293261783","https://openalex.org/W4402349375"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"power":[1,51,68,73,83,98,127,145,150,165,179,204,209,232,242],"estimation":[2,18,184,205,221,233],"early":[3],"in":[4,50,67,84,138,163,240],"the":[5,11,48,56,126,156,176],"design":[6,12],"cycle":[7],"is":[8],"crucial":[9],"for":[10,41,152,189],"of":[13,25,93,102,125,140,178],"power-efficient":[14],"System-on-Chips":[15],"(SoCs).":[16],"Power":[17],"has":[19,37],"been":[20],"researched":[21],"at":[22],"various":[23],"levels":[24],"abstraction,":[26],"with":[27,235],"a":[28,89,111,117,122,229],"well-known":[29],"trade-off":[30],"between":[31],"efficiency":[32],"and":[33,72,78,107,147,194,217],"accuracy.":[34,185],"Recent":[35],"work":[36,162],"shown":[38],"great":[39],"promise":[40],"machine":[42],"learning":[43],"(ML)":[44],"techniques":[45],"to":[46,115,135,174,200],"advance":[47],"state-of-the-art":[49,202],"estimation.":[52],"We":[53,186],"propose":[54],"ML-Power,":[55],"first":[57],"ML-based":[58,149,164,203],"framework":[59],"that":[60,81],"can":[61],"address":[62],"both":[63],"key":[64,104],"bottlenecks":[65],"involved":[66],"estimation,":[69],"viz.":[70],"simulation":[71,133],"model":[74,114],"evaluation.":[75],"ML-Power":[76,100,188,223],"builds":[77],"uses":[79],"models":[80,134,151,210],"estimate":[82],"each":[85],"SoC":[86,172,193],"block":[87],"using":[88],"very":[90],"small":[91],"subset":[92],"internal":[94],"signals,":[95,129],"known":[96],"as":[97],"proxies.":[99],"consists":[101],"two":[103,195],"components,":[105],"PACE":[106,109],"SCOPE.":[108],"trains":[110,148,208],"sequence-to-sequence":[112],"ML":[113],"translate":[116],"transaction-level":[118],"execution":[119],"trace":[120,124],"into":[121],"cycle-level":[123],"proxy":[128],"allowing":[130],"much":[131],"faster":[132],"be":[136],"used":[137],"place":[139],"RTL":[141,231],"simulation.":[142],"SCOPE":[143,158,207],"selects":[144],"proxies":[146,180],"blocks":[153,173],"(IPs)":[154],"within":[155],"SoC.":[157],"improves":[159],"upon":[160],"prior":[161,201],"modeling":[166],"by":[167],"exploiting":[168],"spatio-temporal":[169],"correlations":[170],"among":[171],"minimize":[175],"number":[177],"while":[181],"also":[182,224],"improving":[183],"evaluate":[187],"an":[190],"ARM":[191],"SSE-300":[192],"RISC-V":[196],"based":[197],"SoCs.":[198],"Compared":[199],"frameworks,":[206],"\u223c65\u00d7":[211],"faster,":[212],"picks":[213],"40%":[214],"fewer":[215],"proxies,":[216],"achieves":[218,225],"2%":[219],"lower":[220],"error.":[222],"\u223c300\u00d7":[226],"speedup":[227],"over":[228],"commercial":[230],"tool":[234],"less":[236],"than":[237],"7%":[238],"error":[239],"per-cycle":[241],"estimates.":[243]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-03T00:00:00"}