{"id":"https://openalex.org/W3041118795","doi":"https://doi.org/10.1109/isqed48828.2020.9137013","title":"CSCMAC - Cyclic Sparsely Connected Neural Network Manycore Accelerator","display_name":"CSCMAC - Cyclic Sparsely Connected Neural Network Manycore Accelerator","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3041118795","doi":"https://doi.org/10.1109/isqed48828.2020.9137013","mag":"3041118795"},"language":"en","primary_location":{"id":"doi:10.1109/isqed48828.2020.9137013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed48828.2020.9137013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 21st International Symposium on Quality Electronic Design (ISQED)","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/A5088024710","display_name":"Hirenkumar Paneliya","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hirenkumar Paneliya","raw_affiliation_strings":["University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101892993","display_name":"Morteza Hosseini","orcid":"https://orcid.org/0000-0002-7218-7754"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Morteza Hosseini","raw_affiliation_strings":["University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060036961","display_name":"Avesta Sasan","orcid":"https://orcid.org/0000-0002-4052-8075"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avesta Sasan","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047382437","display_name":"Houman Homayoun","orcid":"https://orcid.org/0000-0001-8904-4699"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Houman Homayoun","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084010501","display_name":"Tinoosh Mohsenin","orcid":"https://orcid.org/0000-0001-5551-2124"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tinoosh Mohsenin","raw_affiliation_strings":["University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.115,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34545581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"311","last_page":"316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T12676","display_name":"Machine Learning and ELM","score":0.9984999895095825,"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":0.7002774477005005},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5719026923179626},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.34029293060302734},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.33054906129837036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20840978622436523}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7002774477005005},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5719026923179626},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.34029293060302734},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.33054906129837036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20840978622436523}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isqed48828.2020.9137013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed48828.2020.9137013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 21st International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1724438581","https://openalex.org/W1972200139","https://openalex.org/W1996079749","https://openalex.org/W2026297770","https://openalex.org/W2050032102","https://openalex.org/W2119144962","https://openalex.org/W2186843572","https://openalex.org/W2285660444","https://openalex.org/W2289252105","https://openalex.org/W2475287302","https://openalex.org/W2801285442","https://openalex.org/W2804394481","https://openalex.org/W2806076314","https://openalex.org/W2884180697","https://openalex.org/W2884780389","https://openalex.org/W2905686860","https://openalex.org/W2922747391","https://openalex.org/W2923223302","https://openalex.org/W2924168890","https://openalex.org/W2945027786","https://openalex.org/W2945047231","https://openalex.org/W2949760349","https://openalex.org/W2962760064","https://openalex.org/W2963674932","https://openalex.org/W2964299589","https://openalex.org/W2977691838","https://openalex.org/W3000129980","https://openalex.org/W3013550173","https://openalex.org/W3029362297","https://openalex.org/W3041420614","https://openalex.org/W4289441319","https://openalex.org/W4298634571","https://openalex.org/W6760639414","https://openalex.org/W6775354534"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,191],"energy-efficient,":[4],"domain-specific":[5],"manycore":[6,80,116,214],"accelerator":[7],"also":[8],"referred":[9],"to":[10,62,68,210,225],"as":[11],"the":[12,51,115,127,131,174,217,229],"\u201cCSCMAC\u201d":[13],"-":[14],"Cyclic":[15],"Sparsely":[16],"Connected":[17],"Neural":[18],"Network":[19],"Manycore":[20],"Accelerator,":[21],"which":[22,48],"effectively":[23],"maps":[24],"and":[25,45,65,88,125,141,179,221],"executes":[26],"deep":[27],"neural":[28],"networks":[29],"(DNNs)":[30],"compressed":[31],"with":[32,156],"cyclic":[33,86],"sparsely":[34],"connected":[35,56],"(CSC)":[36],"architectures.":[37],"CSC":[38,74,157],"layers":[39,58,63,75,155],"are":[40],"architectures":[41],"that":[42,90,117,151],"structurally":[43],"compress":[44],"sparsify":[46],"DNNs,":[47],"can":[49,160],"reduce":[50],"memory":[52],"footprint":[53],"of":[54,84,106,122,166,173,187,228],"fully":[55,177],"(FC)":[57],"from$O(N^{2})$to$O(N\\log":[59],"N)$with":[60],"respect":[61],"nodes,":[64],"is":[66,100,176],"shown":[67],"be":[69],"hardware":[70],"implementable-friendly.":[71],"We":[72],"implement":[73],"for":[76,96,114],"inference":[77],"on":[78,139,146],"a":[79,97,137,142,164],"unit,":[81],"take":[82,104],"advantage":[83,105],"their":[85,91,107],"architecture,":[87],"show":[89],"implementation":[92,108,227],"in":[93],"software":[94],"even":[95],"parallel-computing":[98],"processor":[99],"affable.":[101],"To":[102],"further":[103],"simplicity,":[109],"we":[110,159],"propose":[111],"customized":[112],"instructions":[113],"fuse":[118],"frequently":[119],"used":[120],"sequences":[121],"machine":[123],"codes":[124],"evaluate":[126],"optimization":[128],"gained":[129],"by":[130,152],"customization.":[132],"Our":[133,202],"experimental":[134],"results":[135],"using":[136],"LeNet300100":[138],"MNIST":[140],"Multi-Layer":[143],"Perceptron":[144],"(MLP)":[145],"Physical":[147],"Activity":[148],"Monitoring":[149],"indicate":[150],"replacing":[153],"FC":[154],"layers,":[158],"achieve$46\\times$and$6\\times$compression":[161],"respectively":[162],"within":[163],"margin":[165],"2%":[167],"accuracy":[168],"loss.":[169],"A":[170],"64-cluster":[171],"architecture":[172],"CSCMAC":[175,204,218],"placed":[178],"routed":[180],"using$65\\mathrm{nm}$,":[181],"TSMC":[182],"CMOS":[183],"technology.":[184],"The":[185],"layout":[186],"each":[188],"cluster":[189],"occupies":[190],"area":[192],"of$0.73\\":[193],"mm^{2}$and":[194],"consumes$230.2":[195],"\\mathrm{mW}$power":[196],"at":[197],"980":[198],"MHz":[199],"clock":[200],"frequency.":[201],"proposed":[203],"achieves$1.48\\times$higher":[205],"throughput":[206,220],"and$1.49\\times$lower":[207],"energy":[208,223],"compared":[209,224],"its":[211],"equivalent":[212],"predecessor":[213],"(PENC).":[215],"Also,":[216],"achieves$85\\times$higher":[219],"consumes$66.4\\times$lower":[222],"CPU":[226],"NVIDIA":[230],"Jetson":[231],"TX2":[232],"platform.":[233]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}