{"id":"https://openalex.org/W2963396654","doi":"https://doi.org/10.1145/3194554.3194625","title":"Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks","display_name":"Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks","publication_year":2018,"publication_date":"2018-05-30","ids":{"openalex":"https://openalex.org/W2963396654","doi":"https://doi.org/10.1145/3194554.3194625","mag":"2963396654"},"language":"en","primary_location":{"id":"doi:10.1145/3194554.3194625","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3194554.3194625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"},"type":"conference-paper","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/A5030060072","display_name":"Caiwen Ding","orcid":"https://orcid.org/0000-0003-0891-1231"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caiwen Ding","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051490998","display_name":"Ao Ren","orcid":"https://orcid.org/0000-0002-2322-8038"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ao Ren","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007300551","display_name":"Geng Yuan","orcid":"https://orcid.org/0000-0001-9844-992X"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Geng Yuan","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016070401","display_name":"Xiaolong Ma","orcid":"https://orcid.org/0000-0003-3753-7648"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolong Ma","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100435411","display_name":"Jiayu Li","orcid":"https://orcid.org/0000-0002-9789-1908"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Li","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100432426","display_name":"Ning Liu","orcid":"https://orcid.org/0000-0003-4943-6625"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ning Liu","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770278","display_name":"Bo Yuan","orcid":"https://orcid.org/0000-0002-3978-2930"},"institutions":[{"id":"https://openalex.org/I125687163","display_name":"City College of New York","ror":"https://ror.org/00wmhkr98","country_code":"US","type":"education","lineage":["https://openalex.org/I125687163"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]},{"id":"https://openalex.org/I4210093530","display_name":"City College","ror":"https://ror.org/00h90tg62","country_code":"US","type":"education","lineage":["https://openalex.org/I4210093530"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Yuan","raw_affiliation_strings":["City University of New York, City College, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of New York, City College, New York, NY, USA","institution_ids":["https://openalex.org/I174216632","https://openalex.org/I4210093530","https://openalex.org/I125687163"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651384","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0000-0002-3024-7990"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"353","last_page":"358"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9991999864578247,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.838444709777832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8029653429985046},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7969032526016235},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6092962622642517},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5153395533561707},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4912678599357605},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.48624053597450256},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47353240847587585},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.47210565209388733},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.4505501389503479},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.43097931146621704},{"id":"https://openalex.org/keywords/matrix-multiplication","display_name":"Matrix multiplication","score":0.4152401387691498},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.40675199031829834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22925657033920288}],"concepts":[{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.838444709777832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8029653429985046},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7969032526016235},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6092962622642517},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5153395533561707},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4912678599357605},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.48624053597450256},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47353240847587585},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.47210565209388733},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4505501389503479},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.43097931146621704},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.4152401387691498},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.40675199031829834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22925657033920288},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3194554.3194625","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3194554.3194625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1489355995","https://openalex.org/W1667072054","https://openalex.org/W1966580635","https://openalex.org/W2007339694","https://openalex.org/W2016053056","https://openalex.org/W2044535169","https://openalex.org/W2048266589","https://openalex.org/W2076063813","https://openalex.org/W2112796928","https://openalex.org/W2118858186","https://openalex.org/W2130325614","https://openalex.org/W2160815625","https://openalex.org/W2183631084","https://openalex.org/W2194775991","https://openalex.org/W2276486856","https://openalex.org/W2285660444","https://openalex.org/W2289252105","https://openalex.org/W2314470091","https://openalex.org/W2335728318","https://openalex.org/W2551814622","https://openalex.org/W2556506018","https://openalex.org/W2585720638","https://openalex.org/W2588861206","https://openalex.org/W2597215741","https://openalex.org/W2606722458","https://openalex.org/W2616152995","https://openalex.org/W2618530766","https://openalex.org/W2657126969","https://openalex.org/W2751366252","https://openalex.org/W2769269352","https://openalex.org/W2950656546","https://openalex.org/W2962820060","https://openalex.org/W2963147303","https://openalex.org/W2963479620","https://openalex.org/W2964299589","https://openalex.org/W3104393472","https://openalex.org/W3118608800","https://openalex.org/W6600234944"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W4389249638","https://openalex.org/W2734358244","https://openalex.org/W4388700941","https://openalex.org/W3015200942"],"abstract_inverted_index":{"Both":[0],"industry":[1],"and":[2,17,39,62,77,87,90,107,112,139,163,190,208],"academia":[3],"have":[4],"extensively":[5],"investigated":[6],"hardware":[7],"accelerations.":[8],"To":[9],"address":[10],"the":[11,25,46,49,117,122,133,151,171,177],"demands":[12],"in":[13,20,110,161,166,185],"increasing":[14],"computational":[15,70],"capability":[16],"memory":[18,148],"requirement,":[19],"this":[21,197],"work,":[22],"we":[23,102],"propose":[24],"structured":[26],"weight":[27],"matrices":[28,54],"(SWM)-based":[29],"compression":[30,63],"technique":[31,67],"for":[32,84,202],"both":[33,88,206],"Field":[34],"Programmable":[35],"Gate":[36],"Array":[37],"(FPGA)":[38],"application-specific":[40],"integrated":[41],"circuit":[42],"(ASIC)":[43],"implementations.":[44],"In":[45],"algorithm":[47],"part,":[48],"SWM-based":[50,66,118,153,179],"framework":[51],"adopts":[52],"block-circulant":[53],"to":[55,74,82,158],"achieve":[56,103,156],"a":[57],"fine-grained":[58],"tradeoff":[59],"between":[60],"accuracy":[61,130],"ratio.":[64],"The":[65],"can":[68,155],"reduce":[69],"complexity":[71,79],"from":[72,80],"O(n2)":[73,81],"O(nlog":[75],"n)":[76],"storage":[78],"O(n)":[83],"each":[85],"layer":[86],"training":[89],"inference":[91],"phases.":[92],"For":[93,141,174],"FPGA":[94,142],"implementations":[95,143],"on":[96,144],"deep":[97],"convolutional":[98],"neural":[99],"networks":[100],"(DCNNs),":[101],"at":[104],"least":[105],"152X":[106],"72X":[108],"improvement":[109],"performance":[111,162],"energy":[113,167,191],"efficiency,":[114],"respectively":[115],"using":[116,132],"framework,":[119],"compared":[120,169],"with":[121,170],"baseline":[123],"of":[124,136,187],"IBM":[125],"TrueNorth":[126],"processor":[127],"under":[128],"same":[129],"constraints":[131],"data":[134],"set":[135],"MNIST,":[137],"SVHN,":[138],"CIFAR-10.":[140],"long":[145],"short":[146],"term":[147],"(LSTM)":[149],"networks,":[150],"proposed":[152,178],"LSTM":[154],"up":[157],"21X":[159],"enhancement":[160],"33.5X":[164],"gains":[165],"efficiency":[168],"ESE":[172],"accelerator.":[173],"ASIC":[175,180],"implementations,":[176],"design":[181],"exhibits":[182],"impressive":[183],"advantages":[184],"terms":[186],"power,":[188],"throughput,":[189],"efficiency.":[192],"Experimental":[193],"results":[194],"indicate":[195],"that":[196],"method":[198],"is":[199],"greatly":[200],"suitable":[201],"applying":[203],"DNNs":[204],"onto":[205],"FPGAs":[207],"mobile/IoT":[209],"devices.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}