{"id":"https://openalex.org/W3198533388","doi":"https://doi.org/10.1109/socc49529.2020.9524770","title":"Processing-in-Memory Accelerator for Dynamic Neural Network with Run-Time Tuning of Accuracy, Power and Latency","display_name":"Processing-in-Memory Accelerator for Dynamic Neural Network with Run-Time Tuning of Accuracy, Power and Latency","publication_year":2020,"publication_date":"2020-09-08","ids":{"openalex":"https://openalex.org/W3198533388","doi":"https://doi.org/10.1109/socc49529.2020.9524770","mag":"3198533388"},"language":"en","primary_location":{"id":"doi:10.1109/socc49529.2020.9524770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/socc49529.2020.9524770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 33rd International System-on-Chip Conference (SOCC)","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/A5100419157","display_name":"Li Yang","orcid":"https://orcid.org/0000-0002-2839-6196"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Li Yang","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036755436","display_name":"Zhezhi He","orcid":"https://orcid.org/0000-0002-6357-236X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhezhi He","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051680668","display_name":"Shaahin Angizi","orcid":"https://orcid.org/0000-0003-2289-6381"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaahin Angizi","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047916979","display_name":"Deliang Fan","orcid":"https://orcid.org/0000-0002-7989-6297"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deliang Fan","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100419157"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.1027,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.47284543,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"117","last_page":"122"},"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":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8323991298675537},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5234046578407288},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.5087087750434875},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4604528546333313},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.45332071185112},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3603505492210388},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.34877705574035645},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.32796207070350647},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3262995481491089},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.23698842525482178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1856546401977539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8323991298675537},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5234046578407288},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.5087087750434875},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4604528546333313},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.45332071185112},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3603505492210388},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.34877705574035645},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.32796207070350647},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3262995481491089},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.23698842525482178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1856546401977539},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/socc49529.2020.9524770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/socc49529.2020.9524770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 33rd International System-on-Chip Conference (SOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G1051472416","display_name":null,"funder_award_id":"2005209,2003749,1931871","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2010202670","https://openalex.org/W2108598243","https://openalex.org/W2119144962","https://openalex.org/W2194775991","https://openalex.org/W2214682759","https://openalex.org/W2396622873","https://openalex.org/W2508602506","https://openalex.org/W2524428287","https://openalex.org/W2553303224","https://openalex.org/W2582745083","https://openalex.org/W2612445135","https://openalex.org/W2765234579","https://openalex.org/W2793867693","https://openalex.org/W2809295488","https://openalex.org/W2810075754","https://openalex.org/W2885077862","https://openalex.org/W2905741102","https://openalex.org/W2919115771","https://openalex.org/W2945829174","https://openalex.org/W2951104886","https://openalex.org/W2952088488","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2963163009","https://openalex.org/W2963766446","https://openalex.org/W2963821229","https://openalex.org/W2964299589","https://openalex.org/W2982644126","https://openalex.org/W2988640543","https://openalex.org/W2998470761","https://openalex.org/W3013202691","https://openalex.org/W3091602279","https://openalex.org/W3092216062","https://openalex.org/W3118608800","https://openalex.org/W4240163901","https://openalex.org/W4255133361","https://openalex.org/W4297775537","https://openalex.org/W6638523607","https://openalex.org/W6677580257","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6727208969","https://openalex.org/W6729956949","https://openalex.org/W6732814185","https://openalex.org/W6737664043","https://openalex.org/W6746582238","https://openalex.org/W6752515464","https://openalex.org/W6757036269","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2307385607","https://openalex.org/W2049261842","https://openalex.org/W2983245704","https://openalex.org/W2390254310","https://openalex.org/W4319952061","https://openalex.org/W4280636456","https://openalex.org/W4388913998","https://openalex.org/W4310584535","https://openalex.org/W4295935044","https://openalex.org/W3159906349"],"abstract_inverted_index":{"With":[0],"the":[1,32,76,147],"widely":[2],"deployment":[3],"of":[4,102,143],"powerful":[5],"deep":[6],"neural":[7,179],"network":[8,56,180],"(DNN)":[9],"into":[10],"smart,":[11],"but":[12,54],"resource":[13,67,91],"limited":[14,68],"IoT":[15],"devices,":[16],"many":[17],"prior":[18,104],"works":[19,105],"have":[20],"been":[21],"proposed":[22],"to":[23,30,78,83,108,114,139,149],"compress":[24],"DNN":[25,49,118,133],"in":[26,47],"a":[27,52,61,87,116],"hardware-aware":[28],"manner":[29],"reduce":[31],"computing":[33,89,137],"complexity,":[34],"while":[35],"maintaining":[36],"accuracy,":[37],"such":[38,73,177],"as":[39],"weight":[40],"quantization,":[41],"pruning,":[42],"convolution":[43],"decomposition,":[44],"etc.":[45],"However,":[46,72],"typical":[48],"compression":[50],"methods,":[51],"smaller,":[53],"fixed,":[55],"structure":[57,81,119],"is":[58],"generated":[59],"from":[60],"relative":[62],"large":[63],"background":[64],"model":[65,158],"for":[66,86,176],"hardware":[69,90],"accelerator":[70,171],"deployment.":[71,159],"optimization":[74],"lacks":[75],"ability":[77,148],"tune":[79,135],"its":[80,136],"on-the-fly":[82,156],"best":[84],"fit":[85],"dynamic":[88,117,132,178],"allocation":[92],"and":[93,154],"workloads.":[94],"In":[95],"this":[96,110],"paper,":[97],"we":[98],"mainly":[99],"review":[100],"two":[101],"our":[103],"[1],":[106],"[2]":[107],"address":[109],"issue,":[111],"discussing":[112],"how":[113],"construct":[115],"through":[120],"either":[121],"uniform":[122],"or":[123],"non-uniform":[124],"channel":[125],"selection":[126],"based":[127,168],"sub-network":[128],"sampling.":[129],"The":[130],"constructed":[131],"could":[134],"path":[138],"involve":[140],"different":[141],"number":[142],"channels,":[144],"thus":[145],"providing":[146],"trade-off":[150],"between":[151],"speed,":[152],"power":[153],"accuracy":[155],"after":[157],"Correspondingly,":[160],"an":[161],"emerging":[162],"Spin-Orbit":[163],"Torque":[164],"Magnetic":[165],"Random-Access-Memory":[166],"(SOT-MRAM)":[167],"Processing-In-Memory":[169],"(PIM)":[170],"will":[172],"also":[173],"be":[174],"discussed":[175],"structure.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
