{"id":"https://openalex.org/W2788838111","doi":"https://doi.org/10.1145/3174243.3174261","title":"DeltaRNN","display_name":"DeltaRNN","publication_year":2018,"publication_date":"2018-02-15","ids":{"openalex":"https://openalex.org/W2788838111","doi":"https://doi.org/10.1145/3174243.3174261","mag":"2788838111"},"language":"en","primary_location":{"id":"doi:10.1145/3174243.3174261","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3174243.3174261","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3174261&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3174261&type=pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082967906","display_name":"Chang Gao","orcid":"https://orcid.org/0000-0002-3284-4078"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Chang Gao","raw_affiliation_strings":["University of Zurich &amp; ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Zurich &amp; ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053813559","display_name":"Daniel Neil","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Daniel Neil","raw_affiliation_strings":["University of Zurich &amp; ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Zurich &amp; ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053685981","display_name":"Enea Ceolini","orcid":"https://orcid.org/0000-0002-2676-0804"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]},{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Enea Ceolini","raw_affiliation_strings":["University of Zurich &amp; ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Zurich &amp; ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053821067","display_name":"Shih\u2010Chii Liu","orcid":"https://orcid.org/0000-0002-7557-045X"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]},{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Shih-Chii Liu","raw_affiliation_strings":["University of Zurich &amp; ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Zurich &amp; ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051651857","display_name":"Tobi Delbr\u00fcck","orcid":"https://orcid.org/0000-0001-5479-1141"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]},{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Tobi Delbruck","raw_affiliation_strings":["University of Zurich &amp; ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Zurich &amp; ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5082967906"],"corresponding_institution_ids":["https://openalex.org/I202697423","https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":15.0616,"has_fulltext":true,"cited_by_count":138,"citation_normalized_percentile":{"value":0.99067194,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9958000183105469,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9904000163078308,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8726236820220947},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8341490030288696},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.6893103122711182},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6093754768371582},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5341132283210754},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.52638840675354},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.46650877594947815},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38579851388931274},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3606581389904022},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35779955983161926},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.21455377340316772},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.20243272185325623}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8726236820220947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8341490030288696},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.6893103122711182},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6093754768371582},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5341132283210754},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.52638840675354},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.46650877594947815},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38579851388931274},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3606581389904022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35779955983161926},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.21455377340316772},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.20243272185325623},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3174243.3174261","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3174243.3174261","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3174261&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},{"id":"pmh:oai:www.zora.uzh.ch:168571","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Gao, Chang; Neil, Daniel; Ceolini, Enea; Liu, Shih-Chii; Delbruck, Tobi  (2018). DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator.  In: 26th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA' 18), Monterey, 25 February 2018 - 27 February 2018. ACM Digital Library, 21-30.","raw_type":"Conference or Workshop Item"},{"id":"doi:10.5167/uzh-168571","is_oa":true,"landing_page_url":"https://doi.org/10.5167/uzh-168571","pdf_url":null,"source":{"id":"https://openalex.org/S7407051291","display_name":"Universit\u00e4t Z\u00fcrich, ZORA","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":""}],"best_oa_location":{"id":"doi:10.1145/3174243.3174261","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3174243.3174261","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3174261&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.44999998807907104}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310967","display_name":"Universidad de Sevilla","ror":"https://ror.org/03yxnpp24"},{"id":"https://openalex.org/F4320315121","display_name":"Samsung Advanced Institute of Technology","ror":null},{"id":"https://openalex.org/F4320321652","display_name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich","ror":"https://ror.org/05a28rw58"},{"id":"https://openalex.org/F4320323874","display_name":"Universit\u00e4t Z\u00fcrich","ror":"https://ror.org/02crff812"},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2788838111.pdf","grobid_xml":"https://content.openalex.org/works/W2788838111.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1603934303","https://openalex.org/W1845051632","https://openalex.org/W1969102784","https://openalex.org/W1981252059","https://openalex.org/W1999085092","https://openalex.org/W2004455575","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2119144962","https://openalex.org/W2124007994","https://openalex.org/W2142343760","https://openalex.org/W2143612262","https://openalex.org/W2157331557","https://openalex.org/W2177436562","https://openalex.org/W2527036487","https://openalex.org/W2529096783","https://openalex.org/W2534720278","https://openalex.org/W2582144945","https://openalex.org/W2585720638","https://openalex.org/W2588448445","https://openalex.org/W2613574453","https://openalex.org/W2623629680","https://openalex.org/W2727238169","https://openalex.org/W2757698722","https://openalex.org/W2766450720","https://openalex.org/W2949229618","https://openalex.org/W2949640717","https://openalex.org/W2950894517","https://openalex.org/W4206821167","https://openalex.org/W4243575863","https://openalex.org/W4247384102","https://openalex.org/W4403278009"],"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/W2076915000","https://openalex.org/W2366961778","https://openalex.org/W1572721274","https://openalex.org/W4241314868"],"abstract_inverted_index":{"Recurrent":[0,117],"Neural":[1],"Networks":[2],"(RNNs)":[3],"are":[4,25],"widely":[5],"used":[6],"in":[7,72],"speech":[8],"recognition":[9],"and":[10,57,130,158],"natural":[11],"language":[12],"processing":[13],"applications":[14],"because":[15,149],"of":[16,33,68,84,114,150,162],"their":[17],"capability":[18],"to":[19,38,139,144],"process":[20],"temporal":[21],"sequences.":[22],"Because":[23],"RNNs":[24],"fully":[26],"connected,":[27],"they":[28],"require":[29],"a":[30,73,85,96,103,111,140,145],"large":[31],"number":[32],"weight":[34],"memory":[35,55],"accesses,":[36],"leading":[37],"high":[39],"power":[40,133],"consumption.":[41],"Recent":[42],"theory":[43],"has":[44],"shown":[45],"that":[46,122],"an":[47],"RNN":[48,113,147],"delta":[49,97,136],"network":[50],"update":[51,137,148],"approach":[52,71],"can":[53],"reduce":[54],"access":[56],"computes":[58],"with":[59],"negligible":[60],"accuracy":[61],"loss.":[62],"This":[63],"paper":[64],"describes":[65],"the":[66,82,89,123,151,155,159],"implementation":[67],"this":[69],"theoretical":[70],"hardware":[74],"accelerator":[75],"called":[76],"\"DeltaRNN\"":[77],"(DRNN).":[78],"The":[79,135],"DRNN":[80,124],"updates":[81],"output":[83],"neuron":[86],"only":[87],"when":[88],"neuron\u00bbs":[90],"activation":[91],"changes":[92],"by":[93,154],"more":[94],"than":[95],"threshold.":[98],"It":[99],"was":[100],"implemented":[101],"on":[102],"Xilinx":[104],"Zynq-7100":[105],"FPGA.":[106],"FPGA":[107],"measurement":[108],"results":[109],"from":[110],"single-layer":[112],"256":[115],"Gated":[116],"Unit":[118],"(GRU)":[119],"neurons":[120],"show":[121],"achieves":[125],"1.2":[126],"TOp/s":[127],"effective":[128],"throughput":[129],"164":[131],"GOp/s/W":[132],"efficiency.":[134],"leads":[138],"5.7x":[141],"speedup":[142],"compared":[143],"conventional":[146],"sparsity":[152],"created":[153],"DN":[156],"algorithm":[157],"zero-skipping":[160],"ability":[161],"DRNN.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":6}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2018-03-06T00:00:00"}
