{"id":"https://openalex.org/W3113276786","doi":"https://doi.org/10.1145/3400302.3415663","title":"HitM","display_name":"HitM","publication_year":2020,"publication_date":"2020-11-02","ids":{"openalex":"https://openalex.org/W3113276786","doi":"https://doi.org/10.1145/3400302.3415663","mag":"3113276786"},"language":"en","primary_location":{"id":"doi:10.1145/3400302.3415663","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400302.3415663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International Conference on Computer-Aided Design","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/A5100451305","display_name":"Bing Li","orcid":"https://orcid.org/0000-0003-0732-2267"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bing Li","raw_affiliation_strings":["Capital Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346965","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0001-5172-4736"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["University of Chinese Academy Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy Science, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100451305"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":0.9247,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.75225735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9979000091552734,"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":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.797834038734436},{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.7733193635940552},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6410335898399353},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6090800762176514},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4303182363510132},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.41788357496261597},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4139617681503296},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.39873450994491577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3425573706626892},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.0792289674282074}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.797834038734436},{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.7733193635940552},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6410335898399353},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6090800762176514},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4303182363510132},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.41788357496261597},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4139617681503296},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39873450994491577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3425573706626892},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0792289674282074},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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":1,"locations":[{"id":"doi:10.1145/3400302.3415663","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400302.3415663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International Conference on Computer-Aided Design","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1653164850","display_name":null,"funder_award_id":"2018138","funder_id":"https://openalex.org/F4320322847","funder_display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences"}],"funders":[{"id":"https://openalex.org/F4320322847","display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","ror":"https://ror.org/031141b54"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1503933356","https://openalex.org/W1686810756","https://openalex.org/W1895577753","https://openalex.org/W1933349210","https://openalex.org/W1994589723","https://openalex.org/W2009464942","https://openalex.org/W2048266589","https://openalex.org/W2108598243","https://openalex.org/W2123151033","https://openalex.org/W2136036867","https://openalex.org/W2163605009","https://openalex.org/W2184188583","https://openalex.org/W2282219577","https://openalex.org/W2325235790","https://openalex.org/W2399958287","https://openalex.org/W2442974303","https://openalex.org/W2508429489","https://openalex.org/W2508602506","https://openalex.org/W2518281301","https://openalex.org/W2558929334","https://openalex.org/W2613989746","https://openalex.org/W2799011136","https://openalex.org/W2811080765","https://openalex.org/W2910506572","https://openalex.org/W2946334728","https://openalex.org/W2998332152","https://openalex.org/W3005285208","https://openalex.org/W4254672563","https://openalex.org/W4301501872"],"related_works":["https://openalex.org/W2054635671","https://openalex.org/W2545245183","https://openalex.org/W2017425642","https://openalex.org/W2350916061","https://openalex.org/W2952918855","https://openalex.org/W1970117475","https://openalex.org/W4396815615","https://openalex.org/W3161624601","https://openalex.org/W2078381924","https://openalex.org/W4381388454"],"abstract_inverted_index":{"With":[0],"the":[1,56,68,83,99,105,122,156,163,169,179,187,190,237,244],"rapid":[2],"progress":[3],"of":[4,58,144,236,243],"artificial":[5],"intelligence":[6],"(AI)":[7],"algorithms,":[8],"multi-modal":[9,28,80,91,115,136,165,199],"deep":[10],"neural":[11,46,51],"networks":[12],"(DNNs)":[13],"have":[14,93],"been":[15,39],"applied":[16],"to":[17,26,42,55,121],"some":[18],"challenging":[19],"tasks,":[20],"e.g.,":[21],"image":[22],"and":[23,32,65,96,148,159,168,186,204,206,215],"video":[24],"description":[25,167],"process":[27],"information":[29,161,188],"from":[30,189],"vision":[31],"language.":[33],"Resistive-memory-based":[34],"processing-in-memory":[35],"(ReRAM-based":[36],"PIM)":[37],"has":[38],"extensively":[40],"studied":[41],"accelerate":[43],"either":[44],"convolutional":[45,63],"network":[47,52],"(CNN)":[48],"or":[49],"recurrent":[50],"(RNN).":[53],"According":[54],"requirements":[57],"their":[59],"core":[60],"layers,":[61,67],"i.e.":[62],"layers":[64,101],"linear":[66],"existing":[69,84],"ReRAM-based":[70,85,111,133,175,212,218],"PIMs":[71],"adopt":[72],"different":[73,202],"optimization":[74,120,171],"schemes":[75],"for":[76,114,135],"them.":[77],"Directly":[78],"deploying":[79],"DNNs":[81,92,116,137,200],"on":[82,104,183],"PIMs,":[86],"however,":[87],"is":[88],"inefficient":[89],"because":[90],"combined":[94],"CNN":[95],"RNN":[97],"where":[98],"primary":[100],"differ":[102],"depending":[103],"specific":[106],"tasks.":[107],"Therefore,":[108],"a":[109,131,139,145,173,210],"high-efficiency":[110],"PIM":[112,134,176,213,219],"design":[113,177,214,220],"necessitates":[117],"an":[118,149,216],"adaptive":[119,150,170],"given":[123],"network.":[124],"In":[125],"this":[126],"work,":[127],"we":[128],"propose":[129],"HitM,":[130],"high-throughput":[132,174],"with":[138,162,201,209],"two-stage":[140],"workflow,":[141],"which":[142],"consists":[143],"static":[146,153,191],"analysis":[147,154],"optimization.":[151],"The":[152,227],"generates":[155],"layer-wise":[157],"resource":[158,225],"computation":[160],"input":[164],"DNN":[166],"produces":[172],"through":[178],"dynamic":[180],"algorithm":[181],"based":[182],"hardware":[184,224,246],"resources":[185],"analysis.":[192],"We":[193],"evaluated":[194],"HitM":[195,232],"using":[196],"several":[197],"popular":[198],"parameters":[203],"structures":[205],"compared":[207],"it":[208],"na\u00efve":[211],"optimal-throughput":[217],"that":[221,231],"assumes":[222],"no":[223],"limitations.":[226],"experimental":[228],"results":[229],"show":[230],"averagely":[233],"achieves":[234],"78.01%":[235],"optimal":[238],"throughput":[239],"while":[240],"consumes":[241],"64.52%":[242],"total":[245],"resources.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-12-21T00:00:00"}
