{"id":"https://openalex.org/W3174090943","doi":"https://doi.org/10.1145/3453688.3461747","title":"A Comprehensive Analysis of Low-Impact Computations in Deep Learning Workloads","display_name":"A Comprehensive Analysis of Low-Impact Computations in Deep Learning Workloads","publication_year":2021,"publication_date":"2021-06-18","ids":{"openalex":"https://openalex.org/W3174090943","doi":"https://doi.org/10.1145/3453688.3461747","mag":"3174090943"},"language":"en","primary_location":{"id":"doi:10.1145/3453688.3461747","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3453688.3461747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Great Lakes Symposium on VLSI","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/A5032417356","display_name":"Hengyi Li","orcid":"https://orcid.org/0000-0003-4112-7297"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hengyi Li","raw_affiliation_strings":["Ritsumeikan University, Kusatsu city, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, Kusatsu city, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114948506","display_name":"Zhichen Wang","orcid":"https://orcid.org/0000-0002-4084-3526"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhichen Wang","raw_affiliation_strings":["Ritsumeikan University, Kusatsu city, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, Kusatsu city, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090899188","display_name":"Xuebin Yue","orcid":"https://orcid.org/0000-0002-4356-9243"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xuebin Yue","raw_affiliation_strings":["Ritsumeikan University, Kusatsu city, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, Kusatsu city, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768918","display_name":"Wenwen Wang","orcid":"https://orcid.org/0000-0003-0840-4846"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenwen Wang","raw_affiliation_strings":["University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000257232","display_name":"Hiroyuki Tomiyama","orcid":"https://orcid.org/0000-0003-1655-7877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomiyama Hiroyuki","raw_affiliation_strings":["Ritusumeikan University, Kusatsu city, Japan"],"affiliations":[{"raw_affiliation_string":"Ritusumeikan University, Kusatsu city, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076579498","display_name":"Lin Meng","orcid":"https://orcid.org/0000-0003-4351-6923"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Lin Meng","raw_affiliation_strings":["Ritsumeikan University, Kusatsu city, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, Kusatsu city, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5032417356"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":1.5371,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.84867647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"385","last_page":"390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9955000281333923,"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/T12676","display_name":"Machine Learning and ELM","score":0.9944999814033508,"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":0.8557292222976685},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7262815237045288},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.7142348289489746},{"id":"https://openalex.org/keywords/branch-predictor","display_name":"Branch predictor","score":0.6138002872467041},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5684665441513062},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.5446107387542725},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49764111638069153},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.48854026198387146},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4854072034358978},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.46253517270088196},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.461199015378952},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.459003210067749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.449567973613739},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44184374809265137},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.41729143261909485},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4002383053302765},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.27571552991867065},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12477719783782959}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8557292222976685},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7262815237045288},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.7142348289489746},{"id":"https://openalex.org/C168522837","wikidata":"https://www.wikidata.org/wiki/Q679552","display_name":"Branch predictor","level":2,"score":0.6138002872467041},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5684665441513062},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.5446107387542725},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49764111638069153},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.48854026198387146},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4854072034358978},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.46253517270088196},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.461199015378952},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.459003210067749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.449567973613739},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44184374809265137},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.41729143261909485},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4002383053302765},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.27571552991867065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12477719783782959},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3453688.3461747","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3453688.3461747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W2129393463","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2612445135","https://openalex.org/W2618530766","https://openalex.org/W2795250396","https://openalex.org/W2883780447","https://openalex.org/W2901839763","https://openalex.org/W2904902077","https://openalex.org/W2949117887","https://openalex.org/W2963125010","https://openalex.org/W2963918968"],"related_works":["https://openalex.org/W4384112194","https://openalex.org/W2783354812","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4390813131","https://openalex.org/W2061007994","https://openalex.org/W2349383066","https://openalex.org/W4328132048","https://openalex.org/W4308259661","https://openalex.org/W1498897948"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"have":[4],"achieved":[5],"great":[6],"successes":[7],"in":[8,43,62,134],"various":[9],"machine":[10,39],"learning":[11,40,99],"tasks":[12],"involving":[13],"a":[14],"wide":[15],"range":[16],"of":[17,107,120,131],"domains.":[18],"Though":[19],"there":[20],"are":[21,34],"multiple":[22],"hardware":[23],"platforms":[24],"available,":[25],"such":[26,49,63],"as":[27,50],"GPUs,":[28],"CPUs,":[29],"FPGAs,":[30],"and":[31,45,56,93,115,129],"etc,":[32,116],"CPUs":[33],"still":[35],"preferred":[36],"choices":[37],"for":[38,159],"applications,":[41],"especially":[42],"low-power":[44],"resource-constrained":[46],"computation":[47,64],"environments":[48,65],"embedded":[51],"systems.":[52],"However,":[53],"the":[54,82,105,118,121,127,135,139,151],"power":[55],"performance":[57,128,152],"efficiency":[58],"become":[59],"critical":[60],"issues":[61],"when":[66],"applying":[67],"DNN":[68],"techniques.":[69],"An":[70],"attractive":[71],"optimization":[72],"to":[73,76,80,157],"DNNs":[74],"is":[75],"remove":[77],"redundant":[78],"computations":[79],"enhance":[81],"execution":[83,119],"efficiency.":[84],"To":[85],"this":[86,88,142],"end,":[87],"paper":[89,143],"conducts":[90],"extensive":[91],"experiments":[92],"analyses":[94],"on":[95,138],"popular":[96],"state-of-the-art":[97],"deep":[98],"models.":[100,122,136],"The":[101],"experimental":[102],"results":[103],"include":[104],"numbers":[106],"instructions,":[108],"branches,":[109],"branch":[110],"prediction":[111],"misses,":[112,114],"cache":[113],"during":[117],"Besides,":[123],"we":[124],"also":[125,144],"investigate":[126],"sparsity":[130],"each":[132],"layer":[133],"Based":[137],"analysis":[140],"results,":[141],"proposes":[145],"an":[146],"instruction-level":[147],"optimization,":[148],"which":[149],"achieves":[150],"improvement":[153],"ranging":[154],"from":[155],"10.26%":[156],"28.0%":[158],"certain":[160],"convolution":[161],"layers.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}