{"id":"https://openalex.org/W2407983593","doi":"https://doi.org/10.1145/2897937.2898060","title":"Perform-ML","display_name":"Perform-ML","publication_year":2016,"publication_date":"2016-05-25","ids":{"openalex":"https://openalex.org/W2407983593","doi":"https://doi.org/10.1145/2897937.2898060","mag":"2407983593"},"language":"en","primary_location":{"id":"doi:10.1145/2897937.2898060","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2897937.2898060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 53rd Annual Design Automation Conference","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/A5070731184","display_name":"Azalia Mirhoseini","orcid":"https://orcid.org/0000-0002-2440-0944"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Azalia Mirhoseini","raw_affiliation_strings":["Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035073741","display_name":"Bita Darvish Rouhani","orcid":"https://orcid.org/0000-0002-8412-4320"},"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":"Bita Darvish Rouhani","raw_affiliation_strings":["UC San Diego"],"affiliations":[{"raw_affiliation_string":"UC San Diego","institution_ids":["https://openalex.org/I2800935791"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059274858","display_name":"Ebrahim M. Songhori","orcid":"https://orcid.org/0000-0001-7450-1594"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ebrahim M. Songhori","raw_affiliation_strings":["Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019931011","display_name":"Farinaz Koushanfar","orcid":"https://orcid.org/0000-0003-0798-3794"},"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":"Farinaz Koushanfar","raw_affiliation_strings":["UC San Diego"],"affiliations":[{"raw_affiliation_string":"UC San Diego","institution_ids":["https://openalex.org/I2800935791"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070731184"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":2.3381,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.92333898,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9983999729156494,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8457587957382202},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6836267709732056},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5085395574569702},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.43446457386016846},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.419607549905777},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3465989828109741},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.23312681913375854},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.09571036696434021}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8457587957382202},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6836267709732056},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5085395574569702},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.43446457386016846},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.419607549905777},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3465989828109741},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.23312681913375854},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.09571036696434021},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2897937.2898060","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2897937.2898060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 53rd Annual Design Automation Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1896160955","https://openalex.org/W1974466705","https://openalex.org/W2007500622","https://openalex.org/W2007800125","https://openalex.org/W2065887373","https://openalex.org/W2091825929","https://openalex.org/W2109449402","https://openalex.org/W2115549875","https://openalex.org/W2116810533","https://openalex.org/W2135046866","https://openalex.org/W2137557016","https://openalex.org/W2160840682","https://openalex.org/W2168748809","https://openalex.org/W2170616854","https://openalex.org/W2189465200","https://openalex.org/W2271695307","https://openalex.org/W2286695212","https://openalex.org/W2290150940","https://openalex.org/W2616038372","https://openalex.org/W2964007277","https://openalex.org/W4213135654","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3205506801","https://openalex.org/W4280599700","https://openalex.org/W2051487156","https://openalex.org/W3183570023","https://openalex.org/W4382323155","https://openalex.org/W4292794827","https://openalex.org/W4224939635"],"abstract_inverted_index":{"We":[0,93],"propose":[1],"Perform-ML,":[2],"the":[3,18,21,25,38,84,99,113,152,155],"first":[4],"Machine":[5],"Learning":[6],"(ML)":[7],"framework":[8],"for":[9,24],"analysis":[10,43],"of":[11,27,41,51,64,83,112,122,132,145,154],"massive":[12,133],"and":[13,54,60,80,128,134],"dense":[14,135],"data":[15,70,85,136],"which":[16,56,86],"customizes":[17],"algorithm":[19],"to":[20,104,124,151],"underlying":[22],"platform":[23,48,109],"purpose":[26],"achieving":[28],"optimized":[29],"resource":[30],"efficiency.":[31],"Perform-ML":[32,65,96,123],"creates":[33],"a":[34,46,67],"performance":[35,91,101,149],"model":[36],"quantifying":[37],"computational":[39],"cost":[40,106],"iterative":[42],"algorithms":[44],"on":[45,137],"pertinent":[47],"in":[49,89,148],"terms":[50],"FLOPs,":[52],"communication,":[53],"memory,":[55],"characterize":[57],"runtime,":[58],"storage,":[59],"energy.":[61],"The":[62],"core":[63],"is":[66],"novel":[68],"parametric":[69],"projection":[71],"algorithm,":[72],"called":[73],"Elastic":[74],"Dictionary":[75],"(ExD),":[76],"that":[77,95],"enables":[78],"versatile":[79],"sparse":[81],"representations":[82],"can":[87,97],"help":[88],"minimizing":[90],"cost.":[92],"show":[94],"achieve":[98],"optimal":[100],"objective,":[102],"according":[103],"our":[105],"model,":[107],"by":[108],"aware":[110],"tuning":[111],"ExD":[114],"parameters.":[115],"An":[116],"accompanying":[117],"API":[118],"ensures":[119],"automated":[120],"applicability":[121],"various":[125],"algorithms,":[126],"datasets,":[127],"platforms.":[129],"Proof-of-concept":[130],"evaluations":[131],"different":[138],"platforms":[139],"demonstrate":[140],"more":[141],"than":[142],"an":[143],"order":[144],"magnitude":[146],"improvements":[147],"compared":[150],"state":[153],"art,":[156],"within":[157],"guaranteed":[158],"user-defined":[159],"error":[160],"bounds.":[161]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}