{"id":"https://openalex.org/W2901663942","doi":"https://doi.org/10.23919/date.2019.8714765","title":"Learning to Skip Ineffectual Recurrent Computations in LSTMs","display_name":"Learning to Skip Ineffectual Recurrent Computations in LSTMs","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2901663942","doi":"https://doi.org/10.23919/date.2019.8714765","mag":"2901663942"},"language":"en","primary_location":{"id":"doi:10.23919/date.2019.8714765","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2019.8714765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Design, Automation & Test in Europe Conference & Exhibition (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/A5064497251","display_name":"Arash Ardakani","orcid":"https://orcid.org/0000-0003-3274-2394"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Arash Ardakani","raw_affiliation_strings":["McGill University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055663061","display_name":"Zhengyun Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zhengyun Ji","raw_affiliation_strings":["McGill University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091600002","display_name":"Warren J. Gross","orcid":"https://orcid.org/0000-0002-6226-6037"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Warren J. Gross","raw_affiliation_strings":["McGill University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064497251"],"corresponding_institution_ids":["https://openalex.org/I5023651"],"apc_list":null,"apc_paid":null,"fwci":1.3282,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.84128811,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1427","last_page":"1432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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":0.9991999864578247,"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/T10320","display_name":"Neural Networks and Applications","score":0.9988999962806702,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987999796867371,"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":0.8071585893630981},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7795435190200806},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.7498754262924194},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6217392086982727},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5934171676635742},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5791340470314026},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5638126134872437},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47036051750183105},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4153953194618225},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.36905813217163086},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35460811853408813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3511802554130554},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.28110817074775696},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08946245908737183}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8071585893630981},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7795435190200806},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.7498754262924194},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6217392086982727},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5934171676635742},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5791340470314026},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5638126134872437},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47036051750183105},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4153953194618225},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.36905813217163086},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35460811853408813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3511802554130554},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28110817074775696},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08946245908737183},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date.2019.8714765","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2019.8714765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1591801644","https://openalex.org/W1632114991","https://openalex.org/W1800356822","https://openalex.org/W1815076433","https://openalex.org/W1895577753","https://openalex.org/W1902934009","https://openalex.org/W2048266589","https://openalex.org/W2064675550","https://openalex.org/W2130942839","https://openalex.org/W2143612262","https://openalex.org/W2147800946","https://openalex.org/W2152839228","https://openalex.org/W2516141709","https://openalex.org/W2585720638","https://openalex.org/W2786951478","https://openalex.org/W2798487653","https://openalex.org/W2800421099","https://openalex.org/W2919115771","https://openalex.org/W2950656546","https://openalex.org/W6607333740","https://openalex.org/W6635446068","https://openalex.org/W6638114406","https://openalex.org/W6638545294","https://openalex.org/W6639657675","https://openalex.org/W6639703010","https://openalex.org/W6679436768","https://openalex.org/W6748131973"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W2097707447"],"abstract_inverted_index":{"Long":[0],"Short-Term":[1],"Memory":[2],"(LSTM)":[3],"is":[4],"a":[5,28,80,118,127,159],"special":[6],"class":[7],"of":[8,24,30,48,55,75,107,120,130,203],"recurrent":[9,132,167],"neural":[10],"network,":[11],"which":[12],"has":[13],"shown":[14],"remarkable":[15],"successes":[16],"in":[17,90,109],"processing":[18],"sequential":[19],"data.":[20],"The":[21],"typical":[22],"architecture":[23],"an":[25],"LSTM":[26],"involves":[27],"set":[29,119],"states":[31,35,92,111,185],"and":[32,42,136,172,190],"gates:":[33],"the":[34,43,46,52,72,87,91,110,131,143,146,166,180,183,188,204,207],"retain":[36,85],"information":[37,89,108],"over":[38,105,117],"arbitrary":[39],"time":[40],"intervals":[41],"gates":[44],"regulate":[45],"flow":[47],"information.":[49,96],"Due":[50],"to":[51,61,84,140,196,200],"recursive":[53],"nature":[54],"LSTMs,":[56,76],"they":[57,149],"are":[58,134],"computationally":[59],"intensive":[60],"deploy":[62],"on":[63],"edge":[64],"devices":[65],"with":[66,153],"limited":[67],"hardware":[68,161],"resources.":[69],"To":[70],"reduce":[71],"computational":[73],"complexity":[74],"we":[77,157],"first":[78],"introduce":[79,158],"method":[81,102],"that":[82,100,126,163,178],"learns":[83],"only":[86],"important":[88],"by":[93,194],"pruning":[94],"redundant":[95],"We":[97],"then":[98],"show":[99,177],"our":[101],"can":[103,137,164],"prune":[104],"90%":[106],"without":[112],"incurring":[113],"any":[114],"accuracy":[115],"degradation":[116],"temporal":[121],"tasks.":[122],"This":[123],"observation":[124],"suggests":[125],"large":[128],"fraction":[129],"computations":[133,168,181,208],"ineffectual":[135],"be":[138],"avoided":[139],"speed":[141],"up":[142,187,195],"process":[144,189],"during":[145],"inference":[147],"as":[148],"involve":[150],"noncontributory":[151],"multiplications/accumulations":[152],"zero-valued":[154],"states.":[155,174,211],"Finally,":[156],"custom":[160],"accelerator":[162,205],"perform":[165],"using":[169,182,209],"both":[170],"sparse":[171,184],"dense":[173,210],"Experimental":[175],"measurements":[176],"performing":[179,206],"speeds":[186],"improves":[191],"energy":[192],"efficiency":[193],"5.2\u00d7":[197],"when":[198],"compared":[199],"implementation":[201],"results":[202]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}