{"id":"https://openalex.org/W2591675147","doi":"https://doi.org/10.1145/3061639.3062311","title":"RESPARC","display_name":"RESPARC","publication_year":2017,"publication_date":"2017-06-13","ids":{"openalex":"https://openalex.org/W2591675147","doi":"https://doi.org/10.1145/3061639.3062311","mag":"2591675147"},"language":"en","primary_location":{"id":"doi:10.1145/3061639.3062311","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3061639.3062311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th Annual Design Automation Conference 2017","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/A5089206756","display_name":"Aayush Ankit","orcid":"https://orcid.org/0000-0003-2827-8306"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aayush Ankit","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032635465","display_name":"Abhronil Sengupta","orcid":"https://orcid.org/0000-0002-5545-4494"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abhronil Sengupta","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050310538","display_name":"Priyadarshini Panda","orcid":"https://orcid.org/0000-0002-4167-6782"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Priyadarshini Panda","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031161187","display_name":"Kaushik Roy","orcid":"https://orcid.org/0009-0002-3375-2877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaushik Roy","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089206756"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.5972,"has_fulltext":false,"cited_by_count":94,"citation_normalized_percentile":{"value":0.9775459,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9975000023841858,"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/T12236","display_name":"Photoreceptor and optogenetics research","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"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":0.8197809457778931},{"id":"https://openalex.org/keywords/crossbar-switch","display_name":"Crossbar switch","score":0.8105366230010986},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.695876955986023},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.6125328540802002},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5869742035865784},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.5865059494972229},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5493313670158386},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5015065670013428},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.45644456148147583},{"id":"https://openalex.org/keywords/von-neumann-architecture","display_name":"Von Neumann architecture","score":0.4477059841156006},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4241907000541687},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3363110423088074},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3247302174568176},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3190743625164032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24832463264465332},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08589553833007812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8197809457778931},{"id":"https://openalex.org/C29984679","wikidata":"https://www.wikidata.org/wiki/Q1929149","display_name":"Crossbar switch","level":2,"score":0.8105366230010986},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.695876955986023},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.6125328540802002},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5869742035865784},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.5865059494972229},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5493313670158386},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5015065670013428},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.45644456148147583},{"id":"https://openalex.org/C80469333","wikidata":"https://www.wikidata.org/wiki/Q189088","display_name":"Von Neumann architecture","level":2,"score":0.4477059841156006},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4241907000541687},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3363110423088074},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3247302174568176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3190743625164032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24832463264465332},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08589553833007812},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3061639.3062311","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3061639.3062311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th Annual Design Automation Conference 2017","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306087","display_name":"Semiconductor Research Corporation","ror":"https://ror.org/047z4n946"},{"id":"https://openalex.org/F4320307102","display_name":"Intel Corporation","ror":"https://ror.org/01ek73717"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1542981317","https://openalex.org/W1604973310","https://openalex.org/W1645800954","https://openalex.org/W1667652561","https://openalex.org/W1980446076","https://openalex.org/W2016922062","https://openalex.org/W2056507634","https://openalex.org/W2067323599","https://openalex.org/W2077586448","https://openalex.org/W2093320793","https://openalex.org/W2107012212","https://openalex.org/W2112796928","https://openalex.org/W2152839228","https://openalex.org/W2160428323","https://openalex.org/W2334564994","https://openalex.org/W2335728318","https://openalex.org/W2508602506","https://openalex.org/W2518281301","https://openalex.org/W2529958663","https://openalex.org/W2618530766","https://openalex.org/W3105606255","https://openalex.org/W3118608800","https://openalex.org/W3147600416","https://openalex.org/W4212788319","https://openalex.org/W4243519499","https://openalex.org/W4254672563"],"related_works":["https://openalex.org/W2612269878","https://openalex.org/W2549656885","https://openalex.org/W2588565308","https://openalex.org/W3089892344","https://openalex.org/W2960220682","https://openalex.org/W4306160710","https://openalex.org/W4313442939","https://openalex.org/W4386227293","https://openalex.org/W4372267706","https://openalex.org/W4387251031"],"abstract_inverted_index":{"Neuromorphic":[0],"computing":[1,23],"using":[2],"post-CMOS":[3],"technologies":[4],"is":[5,157],"gaining":[6],"immense":[7],"popularity":[8],"due":[9],"to":[10,14,93,130,167],"its":[11,75],"promising":[12],"abilities":[13],"address":[15],"the":[16,80,95,107,131,168,174],"memory":[17],"and":[18,34,56,74,87,119],"power":[19],"bottlenecks":[20],"in":[21,98,101,114,141],"von-Neumann":[22],"systems.":[24],"In":[25],"this":[26,65],"paper,":[27],"we":[28],"propose":[29],"RESPARC":[30,63,78,136,156],"-":[31],"a":[32,68,89,102,158,163],"reconfigurable":[33,91],"energy":[35,142],"efficient":[36],"architecture":[37,109,160],"built-on":[38],"Memristive":[39],"Crossbar":[40],"Arrays":[41],"(MCA)":[42],"for":[43,71,84,149,173],"deep":[44],"Spiking":[45],"Neural":[46],"Networks":[47],"(SNNs).":[48],"Prior":[49],"works":[50],"were":[51],"primarily":[52],"focused":[53],"on":[54,61,110,124],"device":[55],"circuit":[57],"implementations":[58],"of":[59,82],"SNNs":[60,112],"crossbars.":[62],"advances":[64],"by":[66],"proposing":[67],"complete":[69],"system":[70],"SNN":[72,100,165],"acceleration":[73],"subsequent":[76],"analysis.":[77],"utilizes":[79],"energy-efficiency":[81],"MCAs":[83],"inner-product":[85],"computation":[86],"realizes":[88],"hierarchical":[90],"design":[92],"incorporate":[94],"data-flow":[96],"patterns":[97],"an":[99],"scalable":[103],"fashion.":[104],"We":[105],"evaluate":[106],"proposed":[108],"different":[111],"ranging":[113],"complexity":[115],"from":[116],"2k-230k":[117],"neurons":[118],"1.2M-5.5M":[120],"synapses.":[121],"Simulation":[122],"results":[123],"these":[125],"networks":[126],"show":[127],"that":[128,161],"compared":[129],"baseline":[132],"digital":[133],"CMOS":[134],"architecture,":[135],"achieves":[137],"500x":[138],"(15x)":[139],"efficiency":[140],"benefits":[143],"at":[144],"300x":[145],"(60x)":[146],"higher":[147],"throughput":[148],"multi-layer":[150],"perceptrons":[151],"(deep":[152],"convolutional":[153],"networks).":[154],"Furthermore,":[155],"technology-aware":[159],"maps":[162],"given":[164,175],"topology":[166],"most":[169],"optimized":[170],"MCA":[171],"size":[172],"crossbar":[176],"technology.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2017-03-16T00:00:00"}
