{"id":"https://openalex.org/W4410584023","doi":"https://doi.org/10.23919/date64628.2025.10992716","title":"CPP-SGS: Cycle-Accurate Power Prediction Framework via SNN and Genetic Signal Selection","display_name":"CPP-SGS: Cycle-Accurate Power Prediction Framework via SNN and Genetic Signal Selection","publication_year":2025,"publication_date":"2025-03-31","ids":{"openalex":"https://openalex.org/W4410584023","doi":"https://doi.org/10.23919/date64628.2025.10992716"},"language":"en","primary_location":{"id":"doi:10.23919/date64628.2025.10992716","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date64628.2025.10992716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Design, Automation & Test in Europe Conference (DATE)","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/A5101632724","display_name":"Tong Liu","orcid":"https://orcid.org/0000-0002-4837-4234"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Tong Liu","raw_affiliation_strings":["Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)"],"affiliations":[{"raw_affiliation_string":"Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113333690","display_name":"Zijun Jiang","orcid":"https://orcid.org/0009-0000-8832-9035"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zijun Jiang","raw_affiliation_strings":["Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)"],"affiliations":[{"raw_affiliation_string":"Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014819244","display_name":"Yangdi Lyu","orcid":"https://orcid.org/0000-0001-8322-156X"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yangdi Lyu","raw_affiliation_strings":["Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)"],"affiliations":[{"raw_affiliation_string":"Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101632724"],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11618464,"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":"2"},"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":0.7842000126838684,"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":0.7842000126838684,"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/T10573","display_name":"Power Quality and Harmonics","score":0.7601000070571899,"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/T10663","display_name":"Advanced Battery Technologies Research","score":0.680400013923645,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/selection","display_name":"Selection (genetic algorithm)","score":0.6823278069496155},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6071377992630005},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4856087267398834},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4367862641811371},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19823145866394043},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0593796968460083}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6823278069496155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6071377992630005},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4856087267398834},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4367862641811371},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19823145866394043},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0593796968460083},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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:10.23919/date64628.2025.10992716","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date64628.2025.10992716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Design, Automation & Test in Europe Conference (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W3006966652","https://openalex.org/W4231702991","https://openalex.org/W6987020231"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Effective":[0],"power":[1,15,19,51,82],"management":[2,20],"is":[3],"crucial":[4],"for":[5,80],"optimizing":[6],"the":[7,60,65,71,94],"performance":[8],"and":[9,36,44,74],"longevity":[10],"of":[11,53,105],"integrated":[12],"circuits.":[13],"Cycle-accurate":[14,27],"prediction":[16],"can":[17],"help":[18],"during":[21],"runtime.":[22],"This":[23],"paper":[24],"introduces":[25],"a":[26,98],"Power":[28],"Prediction":[29],"framework":[30],"via":[31],"Spiking":[32],"neural":[33],"networks":[34],"(SNNs)":[35],"Genetic":[37,45],"signal":[38],"Selection":[39],"(CPP-SGS),":[40],"which":[41],"integrates":[42],"SNNs":[43,68],"Algorithms":[46],"(GAs)":[47],"to":[48,58,67,69],"predict":[49],"real-time":[50,85],"consumption":[52],"chips.":[54],"We":[55],"apply":[56],"GAs":[57],"select":[59],"most":[61],"relevant":[62],"signals":[63],"as":[64],"input":[66],"reduce":[70],"model":[72],"size":[73],"inference":[75],"time,":[76],"making":[77],"it":[78],"well-suited":[79],"dynamic":[81],"estimation":[83],"in":[84],"scenarios.":[86],"The":[87],"experimental":[88],"results":[89],"show":[90],"that":[91],"CCP-SGS":[92],"outperforms":[93],"state-of-the-art":[95],"approaches,":[96],"with":[97],"normalized":[99],"root":[100],"mean":[101],"squared":[102],"error":[103],"(NRMSE)":[104],"less":[106],"than":[107],"1.6%.":[108]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}