{"id":"https://openalex.org/W4415250865","doi":"https://doi.org/10.1109/hpec67600.2025.11196603","title":"Encoded time-series model training with U-Net running on Wafer Scale Engine","display_name":"Encoded time-series model training with U-Net running on Wafer Scale Engine","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4415250865","doi":"https://doi.org/10.1109/hpec67600.2025.11196603"},"language":"en","primary_location":{"id":"doi:10.1109/hpec67600.2025.11196603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec67600.2025.11196603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5080441627","display_name":"Vyacheslav Romanov","orcid":"https://orcid.org/0000-0002-8850-3539"},"institutions":[{"id":"https://openalex.org/I1330989302","display_name":"United States Department of Energy","ror":"https://ror.org/01bj3aw27","country_code":"US","type":"government","lineage":["https://openalex.org/I1330989302"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vyacheslav Romanov","raw_affiliation_strings":["NETL,U.S. Department of Energy,Pittsburgh,USA"],"affiliations":[{"raw_affiliation_string":"NETL,U.S. Department of Energy,Pittsburgh,USA","institution_ids":["https://openalex.org/I1330989302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080441627"],"corresponding_institution_ids":["https://openalex.org/I1330989302"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15086455,"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/T10320","display_name":"Neural Networks and Applications","score":0.7620999813079834,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10320","display_name":"Neural Networks and Applications","score":0.7620999813079834,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.7253000140190125,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.7215999960899353,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/workflow","display_name":"Workflow","score":0.6053000092506409},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5187000036239624},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5063999891281128},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.49000000953674316},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4896000027656555},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4887000024318695},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4867999851703644},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3928999900817871}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615999937057495},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6053000092506409},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5187000036239624},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.49000000953674316},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4887000024318695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45350000262260437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3928999900817871},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3594000041484833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3589000105857849},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.30489999055862427},{"id":"https://openalex.org/C152588345","wikidata":"https://www.wikidata.org/wiki/Q6498959","display_name":"Scale model","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.2831999957561493},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26249998807907104},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec67600.2025.11196603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec67600.2025.11196603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320333450","display_name":"National Energy Technology Laboratory","ror":"https://ror.org/01x26mz03"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1577729709","https://openalex.org/W1901129140","https://openalex.org/W1999351305","https://openalex.org/W2008559335","https://openalex.org/W2065275311","https://openalex.org/W2338402873","https://openalex.org/W2942654387","https://openalex.org/W3127393641","https://openalex.org/W3185336267","https://openalex.org/W4312455009","https://openalex.org/W4388561306","https://openalex.org/W4390188604","https://openalex.org/W4414120055"],"related_works":[],"abstract_inverted_index":{"U-Net-based":[0],"models,":[1],"traditionally":[2],"used":[3,41],"for":[4,10,42],"image":[5,85,98],"segmentation,":[6],"can":[7],"be":[8],"adapted":[9],"time":[11,28,47,96],"series":[12,29,48],"by":[13,17,25],"integrating":[14],"attention":[15],"mechanisms,":[16],"replacing":[18],"2D":[19,32,53],"convolutions":[20],"with":[21],"linear":[22],"complexity,":[23],"or":[24],"transforming":[26],"univariate":[27],"data":[30,49],"into":[31],"representations.":[33],"In":[34],"this":[35],"work,":[36],"the":[37,59,79,88,91,101],"U-Net":[38],"architecture":[39],"was":[40,61],"regression":[43],"model":[44,92,103],"training,":[45],"where":[46],"were":[50],"encoded":[51],"in":[52],"images":[54],"and":[55,87,94,100],"compute":[56],"acceleration":[57],"of":[58,81,90],"training":[60,95],"achieved":[62],"through":[63],"massive":[64],"workflow":[65],"parallelism":[66],"on":[67,97],"Cerebras":[68],"wafer":[69],"scale":[70],"engine.":[71],"Geological":[72],"reservoir":[73],"modeling":[74],"as":[75],"case":[76],"study":[77],"demonstrated":[78],"challenges":[80],"processing":[82],"dissimilar":[83],"input-output":[84],"pairs":[86],"effects":[89],"complexity":[93],"\"ghosting\"":[99],"overall":[102],"quality.":[104]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-16T00:00:00"}
