{"id":"https://openalex.org/W3083511631","doi":"https://doi.org/10.1145/3386263.3407646","title":"Deep Neural Network accelerator with Spintronic Memory","display_name":"Deep Neural Network accelerator with Spintronic Memory","publication_year":2020,"publication_date":"2020-09-04","ids":{"openalex":"https://openalex.org/W3083511631","doi":"https://doi.org/10.1145/3386263.3407646","mag":"3083511631"},"language":"en","primary_location":{"id":"doi:10.1145/3386263.3407646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386263.3407646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 on 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/A5100420104","display_name":"He Zhang","orcid":"https://orcid.org/0000-0001-9262-3106"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Zhang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381646","display_name":"Wang Kang","orcid":"https://orcid.org/0000-0002-3169-6034"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Kang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018693228","display_name":"Youguang Zhang","orcid":"https://orcid.org/0009-0008-0928-4210"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youguang Zhang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109540745","display_name":"Weisheng Zhao","orcid":"https://orcid.org/0000-0002-3169-6034"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weisheng Zhao","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"51"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"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":0.9998999834060669,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9961000084877014,"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":0.7550164461135864},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5663225650787354},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5271579623222351},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4759090542793274},{"id":"https://openalex.org/keywords/sense-amplifier","display_name":"Sense amplifier","score":0.4476897418498993},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.4139573574066162},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3376159071922302},{"id":"https://openalex.org/keywords/semiconductor-memory","display_name":"Semiconductor memory","score":0.2392905056476593},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.21356096863746643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.139082133769989},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11400958895683289}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7550164461135864},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5663225650787354},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5271579623222351},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4759090542793274},{"id":"https://openalex.org/C32666082","wikidata":"https://www.wikidata.org/wiki/Q7450979","display_name":"Sense amplifier","level":3,"score":0.4476897418498993},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4139573574066162},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3376159071922302},{"id":"https://openalex.org/C98986596","wikidata":"https://www.wikidata.org/wiki/Q1143031","display_name":"Semiconductor memory","level":2,"score":0.2392905056476593},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.21356096863746643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.139082133769989},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11400958895683289},{"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:10.1145/3386263.3407646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386263.3407646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 on 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":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2943669549"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"Utilizing":[0],"emerging":[1],"nonvolatile":[2],"memories":[3,35],"to":[4,19,40,59,62,102,123,139,227,257,262,298],"accelerate":[5],"deep":[6],"neural":[7,214],"network":[8,215],"(DNN)":[9],"has":[10],"been":[11],"considered":[12],"as":[13,85],"one":[14,78],"of":[15,23,105,136,143,160,179,212,243,281,345],"the":[16,21,27,60,91,106,109,128,133,141,158,169,173,180,205,213,219,224,228,241,247,251,255,258,267,273,279,282,289,299,304,341],"promising":[17],"approaches":[18],"solve":[20],"bottleneck":[22],"data":[24,295],"transfer":[25],"during":[26,331],"multiplication":[28],"and":[29,50,99,121,154,176,194],"accumulation":[30],"(MAC).":[31],"Among":[32],"them,":[33],"spintronic":[34,350,368],"show":[36],"tempting":[37],"prospect":[38],"due":[39],"their":[41],"low":[42],"access":[43,46],"power,":[44],"fast":[45],"speed,":[47],"high":[48,151],"density,":[49],"relatively":[51],"mature":[52],"process.":[53],"As":[54],"shown":[55,86],"in":[56,87,115,190,218,250,266],"fig.1,":[57],"according":[58],"principle":[61],"achieve":[63,140,150,236],"DNN":[64,346,357],"computing,":[65],"it":[66,233,318],"can":[67,112,131,148,234,319,363],"be":[68,113,360,364],"mainly":[69],"divided":[70],"into":[71,95],"three":[72],"different":[73,103,116,137,367],"technical":[74],"routes.":[75],"The":[76,185,210,293],"first":[77],"is":[79,240,260,286,296],"an":[80],"\"analog\"":[81],"method":[82,147,208],"[1,":[83],"2],":[84],"fig.1(a).":[88],"By":[89,222],"transforming":[90],"digital":[92,124],"input":[93,225,294],"signals":[94,308],"multi-level":[96],"voltage":[97],"signals,":[98],"applying":[100],"them":[101],"columns":[104,117],"memory":[107,220,252,329],"array,":[108],"MAC":[110,197],"results":[111],"obtained":[114],"with":[118,246,366],"current":[119],"integrator":[120],"analog":[122],"converter":[125],"(ADC).":[126],"Besides,":[127,172],"WL":[129,305],"drivers":[130],"control":[132],"pulse":[134],"width":[135],"rows,":[138],"effect":[142],"multi-bit":[144],"weights.":[145],"This":[146],"theoretically":[149],"energy":[152],"efficiency":[153],"computing":[155,170,198],"speed.":[156],"However,":[157],"variation":[159],"magnetic":[161],"tunnel":[162],"junction":[163],"(MTJ)":[164],"may":[165],"have":[166],"influence":[167],"on":[168,288,313,349,356],"accuracy.":[171],"power":[174],"consumption":[175],"area":[177],"overhead":[178],"ADC":[181],"are":[182,189,216],"also":[183,235],"challenging.":[184],"other":[186],"two":[187],"methods":[188],"a":[191,314,332],"\"digital\"":[192],"way,":[193],"they":[195],"realize":[196,320],"through":[199],"row-by-row":[200],"read/write":[201],"operation.":[202],"Fig.1(b)":[203],"shows":[204,278],"second":[206],"reading-based":[207],"[3].":[209],"weights":[211],"stored":[217,249],"cell.":[221,253],"putting":[223],"signal":[226],"modified":[229,300],"sensing":[230],"amplifier":[231],"(SA),":[232],"XOR":[237,321],"function,":[238],"which":[239,270,285,362],"core":[242],"binary":[244],"NN,":[245],"content":[248],"Nevertheless,":[254],"modification":[256],"SA":[259],"usually":[261],"add":[263],"extra":[264],"transistors":[265],"read":[268],"path,":[269],"will":[271,339,359],"increase":[272],"bit":[274],"error":[275],"rate.":[276],"Fig.1(c)":[277],"diagram":[280],"last":[283],"one,":[284],"based":[287,348],"\"stateful":[290],"logic\"":[291],"[4].":[292],"sent":[297],"write":[301,333],"driver":[302],"when":[303],"receiving":[306],"weight":[307],"from":[309],"outside":[310],"I/O.":[311],"Based":[312],"unique":[315],"logic":[316],"paradigm,":[317],"function":[322],"for":[323],"BNN":[324],"within":[325],"1":[326],"or":[327],"several":[328],"cells":[330],"cycle.":[334],"In":[335],"this":[336],"talk,":[337],"we":[338],"review":[340],"main":[342],"research":[343],"status":[344],"accelerators":[347],"memories.":[351,369],"Particularly,":[352],"our":[353],"recent":[354],"work":[355],"accelerating":[358],"introduced,":[361],"implemented":[365]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}
