{"id":"https://openalex.org/W7117660128","doi":"https://doi.org/10.1109/mcsoc67473.2025.00106","title":"Energy-Efficient and Accurate Stochastic Computing-Based Multiply-Accumulate Architecture for Neural Network Accelerator Design","display_name":"Energy-Efficient and Accurate Stochastic Computing-Based Multiply-Accumulate Architecture for Neural Network Accelerator Design","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7117660128","doi":"https://doi.org/10.1109/mcsoc67473.2025.00106"},"language":null,"primary_location":{"id":"doi:10.1109/mcsoc67473.2025.00106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mcsoc67473.2025.00106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","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/A5091910865","display_name":"Chia-Heng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chia-Heng Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121632635","display_name":"Hsuan-Yu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsuan-Yu Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121689434","display_name":"Bo-Chun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo-Chun Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121684710","display_name":"Kun-Chih Jimmy Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun-Chih Jimmy Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61279374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"645","last_page":"652"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.8392999768257141,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11321","display_name":"Error Correcting Code Techniques","score":0.8392999768257141,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.026000000536441803,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.01140000019222498,"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/stochastic-computing","display_name":"Stochastic computing","score":0.8867999911308289},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6040999889373779},{"id":"https://openalex.org/keywords/adder","display_name":"Adder","score":0.5430999994277954},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5249999761581421},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5023000240325928},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4702000021934509},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4059000015258789}],"concepts":[{"id":"https://openalex.org/C2780971903","wikidata":"https://www.wikidata.org/wiki/Q2933705","display_name":"Stochastic computing","level":3,"score":0.8867999911308289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7602999806404114},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6040999889373779},{"id":"https://openalex.org/C164620267","wikidata":"https://www.wikidata.org/wiki/Q376953","display_name":"Adder","level":3,"score":0.5430999994277954},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5249999761581421},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.3555000126361847},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3239000141620636},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3046000003814697},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2712000012397766},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2623000144958496}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mcsoc67473.2025.00106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mcsoc67473.2025.00106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8925139307975769,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2020217519","https://openalex.org/W2098470668","https://openalex.org/W2112796928","https://openalex.org/W2151770284","https://openalex.org/W2514875757","https://openalex.org/W2587815976","https://openalex.org/W2607445385","https://openalex.org/W2753188468","https://openalex.org/W2770413419","https://openalex.org/W2924292441","https://openalex.org/W2946695067","https://openalex.org/W2963671426","https://openalex.org/W2969580338","https://openalex.org/W3009570900","https://openalex.org/W3021951958","https://openalex.org/W3132191748","https://openalex.org/W3198281325","https://openalex.org/W4320018421","https://openalex.org/W4400232371"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,31,45,60,66,116,147,182],"high":[2,17],"computational":[3],"complexity,":[4],"the":[5,71,76,89,118,127,152,171,175,184],"design":[6],"of":[7],"Deep":[8],"Neural":[9],"Network":[10],"(DNN)":[11],"accelerators":[12],"faces":[13],"significant":[14,67],"challenges,":[15],"including":[16],"power":[18,130],"consumption":[19],"and":[20,58,110,129,140,160],"considerable":[21],"area":[22,128],"overhead.":[23],"While":[24],"Stochastic":[25],"Computing":[26],"(SC)":[27],"provides":[28],"a":[29,111,134],"way":[30],"reduce":[32,126,156],"hardware":[33,100,141],"overhead":[34],"in":[35,70,80,84,143],"DNN":[36],"accelerator":[37],"design,":[38,151],"it":[39],"has":[40],"lower":[41],"computing":[42,68,82],"accuracy":[43,77,187],"compared":[44],"traditional":[46,148],"methods.":[47],"The":[48,102],"reason":[49],"is":[50],"that":[51],"randomized":[52],"bit-stream":[53],"representations":[54],"introduce":[55,88],"statistical":[56],"errors":[57,69,120],"sensitivity":[59],"correlation":[61],"between":[62],"input":[63],"streams,":[64],"leading":[65],"multiplyaccumulate":[72],"operation.":[73],"To":[74,124],"mitigate":[75],"limitations":[78],"inherent":[79],"stochastic":[81],"(SC),":[83],"this":[85,144],"work,":[86],"we":[87,132],"Integrated":[90],"Multiply-Accumulate":[91],"(IMAC)":[92],"architecture,":[93],"which":[94],"enhances":[95],"precision":[96],"while":[97],"maintaining":[98],"SC's":[99],"efficiency.":[101],"IMAC":[103,154,173,181],"uses":[104],"an":[105],"Accumulating":[106],"Bipolar-to-Unipolar":[107],"Converter":[108],"(ABUC)":[109],"Correlating":[112],"Linear":[113],"Gain":[114],"(CorrGain)":[115],"correct":[117],"scaling":[119],"from":[121],"SC":[122],"adders.":[123],"further":[125],"overhead,":[131],"employ":[133],"toggle":[135],"flip-flop":[136],"(TFF)-based":[137],"adder":[138],"tree":[139],"sharing":[142],"work.":[145],"Compared":[146],"SC-based":[149],"MAC":[150],"proposed":[153,172],"can":[155],"MSE":[157],"by":[158,164],"64.3%-90.6%":[159],"improves":[161],"throughput-to-error":[162],"ratio":[163],"<tex":[165],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[166],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$2.77":[167],"\\times-10.68":[168],"\\times$</tex>.":[169],"Integrating":[170],"into":[174],"LeNet-5":[176,179,190],"architecture":[177],"allows":[178],"with":[180],"achieve":[183],"same":[185],"inference":[186],"as":[188],"conventional":[189],"without":[191],"IMAC.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-31T00:00:00"}
