{"id":"https://openalex.org/W2907544761","doi":"https://doi.org/10.1109/sips.2018.8598348","title":"Selective Data Transfer from DRAMs for CNNs","display_name":"Selective Data Transfer from DRAMs for CNNs","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2907544761","doi":"https://doi.org/10.1109/sips.2018.8598348","mag":"2907544761"},"language":"en","primary_location":{"id":"doi:10.1109/sips.2018.8598348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips.2018.8598348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Workshop on Signal Processing Systems (SiPS)","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/A5045660343","display_name":"Anaam Ansari","orcid":"https://orcid.org/0000-0002-4081-1225"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anaam Ansari","raw_affiliation_strings":["Department of Electrical Engineering, Santa Clara University, Santa Clara, California"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Santa Clara University, Santa Clara, California","institution_ids":["https://openalex.org/I16269868"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025538938","display_name":"Tokunbo Ogunfunmi","orcid":"https://orcid.org/0000-0003-3517-9779"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tokunbo Ogunfunmi","raw_affiliation_strings":["Department of Electrical Engineering, Santa Clara University, Santa Clara, California"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Santa Clara University, Santa Clara, California","institution_ids":["https://openalex.org/I16269868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045660343"],"corresponding_institution_ids":["https://openalex.org/I16269868"],"apc_list":null,"apc_paid":null,"fwci":0.3134,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63765204,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9997000098228455,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8296359777450562},{"id":"https://openalex.org/keywords/dram","display_name":"Dram","score":0.635805606842041},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5458533763885498},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.4872566759586334},{"id":"https://openalex.org/keywords/memory-controller","display_name":"Memory controller","score":0.481989324092865},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.4233260154724121},{"id":"https://openalex.org/keywords/memory-architecture","display_name":"Memory architecture","score":0.4214509129524231},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4178970456123352},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33967018127441406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27974817156791687},{"id":"https://openalex.org/keywords/semiconductor-memory","display_name":"Semiconductor memory","score":0.2596573233604431}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8296359777450562},{"id":"https://openalex.org/C7366592","wikidata":"https://www.wikidata.org/wiki/Q1255620","display_name":"Dram","level":2,"score":0.635805606842041},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5458533763885498},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4872566759586334},{"id":"https://openalex.org/C100800780","wikidata":"https://www.wikidata.org/wiki/Q1175867","display_name":"Memory controller","level":3,"score":0.481989324092865},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4233260154724121},{"id":"https://openalex.org/C2779602883","wikidata":"https://www.wikidata.org/wiki/Q15544750","display_name":"Memory architecture","level":2,"score":0.4214509129524231},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4178970456123352},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33967018127441406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27974817156791687},{"id":"https://openalex.org/C98986596","wikidata":"https://www.wikidata.org/wiki/Q1143031","display_name":"Semiconductor memory","level":2,"score":0.2596573233604431}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sips.2018.8598348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips.2018.8598348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Workshop on Signal Processing Systems (SiPS)","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":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2119144962","https://openalex.org/W2163605009","https://openalex.org/W2233116163","https://openalex.org/W2285660444","https://openalex.org/W2508602506","https://openalex.org/W2509836288","https://openalex.org/W2604319603","https://openalex.org/W2605347906","https://openalex.org/W2606722458","https://openalex.org/W2761105295","https://openalex.org/W2798544842","https://openalex.org/W2798992029","https://openalex.org/W2950656546","https://openalex.org/W2962965870","https://openalex.org/W2964299589","https://openalex.org/W4239722617","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6725588366","https://openalex.org/W6726275242"],"related_works":["https://openalex.org/W1976244802","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W4320351610","https://openalex.org/W2122646225","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNN)":[3],"have":[4,15,45,64],"changed":[5],"the":[6,33,126,140,209,214,241,248,260,263,287,305,311],"direction":[7],"of":[8,35,67,104,148,231,262,296,298,304],"image":[9,54,119],"and":[10,25,30,63,97,106,129,156,164,181,235,285],"speech":[11],"signal":[12],"processing.":[13],"They":[14],"become":[16],"prolific":[17],"in":[18,53,61,93,247,256,271,282,310],"applications":[19],"such":[20],"as":[21,228,250,301],"self":[22,225],"driving":[23],"cars":[24],"voice":[26],"assistants":[27],"like":[28,41],"Siri":[29],"Alexa.":[31],"Since":[32],"success":[34],"AlexNet,":[36],"many":[37],"deep":[38],"learning":[39],"networks":[40,49],"GoogleNet,":[42],"ResidualNet":[43],"etc":[44],"been":[46],"introduced.":[47],"These":[48],"are":[50,58,99,308],"highly":[51],"competent":[52],"classification":[55],"however,":[56],"they":[57],"very":[59],"large":[60],"size":[62],"a":[65,81,133,191,219,224,229,236,251,268,277,302],"lot":[66],"parameters,":[68],"for":[69,280],"example,":[70],"AlexNet":[71,281],"has":[72],"60M":[73],"parameters":[74],"[1].":[75],"On-Off":[76],"chip":[77],"data":[78,108],"transfer":[79,206,273,307],"is":[80,143,169,245],"great":[82],"engineering":[83],"challenge":[84],"that":[85,150,176,199],"needs":[86,120],"to":[87,121,124,152,204,213,258,276,290],"be":[88,130,153],"addressed":[89],"while":[90],"implementing":[91],"CNN":[92,127],"hardware.":[94],"Hardware":[95],"Acceleration":[96],"parallelization":[98],"constrained":[100],"by":[101,145,222],"energy":[102],"cost":[103],"reading":[105],"writing":[107],"from":[109,208],"main":[110],"memory.":[111],"For":[112],"one":[113,118],"forward":[114],"pass":[115],"during":[116,184],"inference,":[117],"go":[122],"through":[123],"all":[125],"layers":[128],"classified":[131],"into":[132],"softmax":[134],"determined":[135],"category.":[136],"During":[137],"this":[138,187],"process,":[139],"memory":[141,174,180,211,272],"bandwidth":[142],"used":[144],"three":[146],"types":[147],"payloads":[149],"need":[151],"moved":[154],"on":[155,171],"off-chip":[157,179],"-":[158],"input":[159],"image,":[160],"intermediate":[161],"feature":[162],"maps,":[163],"filter":[165],"weights.":[166],"This":[167],"research":[168],"focused":[170],"reducing":[172],"weight-related":[173],"traffic":[175,274],"occurs":[177],"between":[178],"on-chip":[182],"buffers":[183],"inference.":[185],"In":[186],"paper,":[188],"we":[189],"propose":[190],"technique":[192],"named":[193],"`weight":[194],"sharing":[195],"selective":[196,288,306],"transfer'":[197],"(WS-ST)":[198],"uses":[200],"processing":[201],"in-memory":[202],"architecture":[203,221],"selectively":[205],"weights":[207],"DRAM":[210,233,244,289],"structure":[212],"computation":[215],"unit.":[216],"We":[217,266],"emulate":[218],"PIM":[220],"having":[223],"populating":[226],"FIFO":[227],"part":[230],"Selective":[232],"controller":[234],"weight":[237,264],"selector":[238],"logic":[239],"near":[240],"DRAM.":[242],"The":[243,293],"modeled":[246],"FPGA":[249],"dual":[252],"port":[253],"static":[254],"RAM":[255],"order":[257],"analyze":[259],"effectiveness":[261],"selector.":[265],"observe":[267],"30%":[269],"decrease":[270],"compared":[275],"non-selective":[278],"approach":[279],"redundancy":[283],"analysis":[284],"use":[286],"implement":[291],"it.":[292],"power":[294,300],"saving":[295],"2%":[297],"dynamic":[299],"result":[303],"reported":[309],"results.":[312]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
