{"id":"https://openalex.org/W3213578211","doi":"https://doi.org/10.1109/access.2021.3126708","title":"Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks","display_name":"Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3213578211","doi":"https://doi.org/10.1109/access.2021.3126708","mag":"3213578211"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3126708","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126708","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09606879.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09606879.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088870796","display_name":"Mahmood Azhar Qureshi","orcid":"https://orcid.org/0000-0001-9995-6907"},"institutions":[{"id":"https://openalex.org/I189590672","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758","country_code":"US","type":"education","lineage":["https://openalex.org/I189590672"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mahmood Azhar Qureshi","raw_affiliation_strings":["Kansas State University, Manhattan, KS, USA"],"affiliations":[{"raw_affiliation_string":"Kansas State University, Manhattan, KS, USA","institution_ids":["https://openalex.org/I189590672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024203425","display_name":"Arslan Munir","orcid":"https://orcid.org/0000-0002-3126-8945"},"institutions":[{"id":"https://openalex.org/I189590672","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758","country_code":"US","type":"education","lineage":["https://openalex.org/I189590672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arslan Munir","raw_affiliation_strings":["Kansas State University, Manhattan, KS, USA"],"affiliations":[{"raw_affiliation_string":"Kansas State University, Manhattan, KS, USA","institution_ids":["https://openalex.org/I189590672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088870796"],"corresponding_institution_ids":["https://openalex.org/I189590672"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6725,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.71848039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"9","issue":null,"first_page":"151458","last_page":"151475"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9943000078201294,"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"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.737391471862793},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6858310699462891},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.42275410890579224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3827306628227234},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37161773443222046},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16665512323379517},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.11804249882698059}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.737391471862793},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6858310699462891},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.42275410890579224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3827306628227234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37161773443222046},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16665512323379517},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.11804249882698059}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3126708","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126708","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09606879.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cb34629c94244243822ec50d59d8eb02","is_oa":true,"landing_page_url":"https://doaj.org/article/cb34629c94244243822ec50d59d8eb02","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 151458-151475 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3126708","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126708","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09606879.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4000000059604645}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310061","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3213578211.pdf","grobid_xml":"https://content.openalex.org/works/W3213578211.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1841592590","https://openalex.org/W1999085092","https://openalex.org/W2000967104","https://openalex.org/W2067523571","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2285660444","https://openalex.org/W2286365479","https://openalex.org/W2289252105","https://openalex.org/W2291160084","https://openalex.org/W2516141709","https://openalex.org/W2518281301","https://openalex.org/W2563587242","https://openalex.org/W2565851976","https://openalex.org/W2593564159","https://openalex.org/W2604319603","https://openalex.org/W2606722458","https://openalex.org/W2612445135","https://openalex.org/W2625457103","https://openalex.org/W2751366252","https://openalex.org/W2759398875","https://openalex.org/W2794260578","https://openalex.org/W2794952988","https://openalex.org/W2887936511","https://openalex.org/W2895531329","https://openalex.org/W2899818272","https://openalex.org/W2904902077","https://openalex.org/W2913104037","https://openalex.org/W2919512338","https://openalex.org/W2945146780","https://openalex.org/W2949619037","https://openalex.org/W2949870694","https://openalex.org/W2963163009","https://openalex.org/W2963374099","https://openalex.org/W2964299589","https://openalex.org/W2973613048","https://openalex.org/W2979042679","https://openalex.org/W2979310060","https://openalex.org/W2979439447","https://openalex.org/W3016542674","https://openalex.org/W3016735325","https://openalex.org/W3016832937","https://openalex.org/W3038838661","https://openalex.org/W3103168911","https://openalex.org/W3104393472","https://openalex.org/W3113177379","https://openalex.org/W3164217046","https://openalex.org/W4240168186","https://openalex.org/W4247198796","https://openalex.org/W4248707617","https://openalex.org/W4297775537","https://openalex.org/W6637373629","https://openalex.org/W6638783484","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6686164453","https://openalex.org/W6687483927","https://openalex.org/W6696004547","https://openalex.org/W6696798448","https://openalex.org/W6734592959","https://openalex.org/W6737664043","https://openalex.org/W6749677685"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2610189143","https://openalex.org/W2159424856","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Sparse":[0,25],"convolutional":[1],"neural":[2],"network":[3],"(CNN)":[4],"models":[5],"reduce":[6],"the":[7,56,66,73,138,163,172,202,205,218],"massive":[8],"compute":[9,198],"and":[10,38,53,69,108,130,140,150,156,166,179,186,266,278],"memory":[11],"bandwidth":[12],"requirements":[13],"inherently":[14],"present":[15,28],"in":[16,23,64,117,177],"dense":[17,139,238],"CNNs":[18],"without":[19],"a":[20,81,128,183,193,208,223,235,246],"significant":[21],"loss":[22],"accuracy.":[24],"CNNs,":[26],"however,":[27,78],"their":[29,87],"own":[30],"set":[31],"of":[32,41,83,135,204,226,249],"challenges":[33],"including":[34],"non-linear":[35],"data":[36],"accesses":[37],"complex":[39,96],"design":[40,98],"CNN":[42,74,120,132],"processing":[43],"elements":[44],"(PEs).":[45],"Recently":[46],"proposed":[47,237],"accelerators":[48],"like":[49],"SCNN,":[50,275],"Eyeriss":[51,94,276],"v2,":[52,277],"SparTen,":[54,279],"exploit":[55],"<italic":[57,124],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[58,125,228,251,259,268],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">two-sided</i>":[59],"sparsity,":[60],"that":[61,85,217],"is,":[62],"sparsity":[63],"both":[65,137],"input":[67],"activations":[68],"weights":[70],"to":[71],"accelerate":[72],"inference.":[75],"These,":[76],"accelerators,":[77,121,243],"suffer":[79],"from":[80],"multitude":[82],"problems":[84],"limit":[86],"applicability,":[88],"such":[89],"as":[90,192],"inefficient":[91],"micro-architecture":[92],"(SCNN,":[93],"v2),":[95,100],"PE":[97],"(Eyeriss":[99],"no":[101],"support":[102],"for":[103],"non-unit":[104],"stride":[105],"convolutions":[106],"(SCNN)":[107],"FC":[109],"layers":[110],"(SparTen,":[111],"SCNN).":[112],"To":[113],"address":[114],"these":[115],"issues":[116],"contemporary":[118],"sparse":[119,141,195,242],"we":[122],"propose":[123],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Sparse-PE</i>":[126],",":[127,257,265],"multi-threaded,":[129],"flexible":[131],"PE,":[133],"capable":[134],"handling":[136],"CNNs.":[142],"The":[143],"Sparse-PE":[144,173,219],"core":[145,174,206],"uses":[146],"binary":[147],"mask":[148],"representation":[149],"actively":[151],"skips":[152],"computations":[153],"involving":[154],"zeros":[155],"favors":[157],"non-zero":[158],"computations,":[159],"thereby,":[160],"drastically":[161],"increasing":[162],"effective":[164],"throughput":[165],"hardware":[167],"utilization.":[168],"Unlike":[169],"previous":[170],"designs,":[171],"is":[175],"generic":[176],"nature":[178],"not":[180],"targeted":[181],"towards":[182],"specific":[184],"accelerator,":[185],"thus,":[187],"can":[188],"also":[189],"be":[190],"used":[191],"standalone":[194],"dot":[196],"product":[197],"engine.":[199],"We":[200],"evaluate":[201],"performance":[203,224,247],"using":[207],"custom":[209],"built":[210],"cycle":[211],"accurate":[212],"simulator.":[213],"Our":[214],"simulations":[215],"show":[216],"core-based":[220],"accelerator":[221,239],"provides":[222,245],"gain":[225,248],"<inline-formula":[227,250,258,267],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[229,252,260,269],"<tex-math":[230,253,261,270],"notation=\"LaTeX\">$12\\times":[231],"$":[232,255,263,272],"</tex-math></inline-formula>":[233,256,264,273],"over":[234,274],"recently":[236],"(NeuroMAX).":[240],"For":[241],"it":[244],"notation=\"LaTeX\">$4.2\\times":[254],"notation=\"LaTeX\">$2.38\\times":[262],"notation=\"LaTeX\">$1.98\\times":[271],"respectively.":[280]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
