{"id":"https://openalex.org/W3083647518","doi":"https://doi.org/10.1145/3386263.3406925","title":"On-chip Memory Optimized CNN Accelerator with Efficient Partial-sum Accumulation","display_name":"On-chip Memory Optimized CNN Accelerator with Efficient Partial-sum Accumulation","publication_year":2020,"publication_date":"2020-09-04","ids":{"openalex":"https://openalex.org/W3083647518","doi":"https://doi.org/10.1145/3386263.3406925","mag":"3083647518"},"language":"en","primary_location":{"id":"doi:10.1145/3386263.3406925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386263.3406925","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":"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/A5101892614","display_name":"Hongjie Xu","orcid":"https://orcid.org/0000-0001-9947-7789"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hongjie Xu","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107461620","display_name":"Jun Shiomi","orcid":"https://orcid.org/0000-0003-2733-9349"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun Shiomi","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026865951","display_name":"Hidetoshi Onodera","orcid":"https://orcid.org/0000-0001-5198-0668"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hidetoshi Onodera","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101892614"],"corresponding_institution_ids":["https://openalex.org/I22299242"],"apc_list":null,"apc_paid":null,"fwci":0.7816,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74349667,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"521","issue":null,"first_page":"21","last_page":"26"},"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":0.998199999332428,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9969000220298767,"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/dataflow","display_name":"Dataflow","score":0.942802906036377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7956950068473816},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7244754433631897},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.7021877765655518},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6829267144203186},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.6483691930770874},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5824295878410339},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.46152961254119873},{"id":"https://openalex.org/keywords/buffer","display_name":"Buffer (optical fiber)","score":0.4246561527252197},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4230368733406067},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18457135558128357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.11813169717788696},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.08024075627326965},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.06364187598228455}],"concepts":[{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.942802906036377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7956950068473816},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7244754433631897},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.7021877765655518},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6829267144203186},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.6483691930770874},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5824295878410339},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.46152961254119873},{"id":"https://openalex.org/C145018004","wikidata":"https://www.wikidata.org/wiki/Q4985944","display_name":"Buffer (optical fiber)","level":2,"score":0.4246561527252197},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4230368733406067},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18457135558128357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11813169717788696},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08024075627326965},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.06364187598228455},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3386263.3406925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386263.3406925","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","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1005811612","https://openalex.org/W1686810756","https://openalex.org/W2088079057","https://openalex.org/W2150073849","https://openalex.org/W2163605009","https://openalex.org/W2257979135","https://openalex.org/W2565960208","https://openalex.org/W2604319603","https://openalex.org/W2606722458","https://openalex.org/W2612445135","https://openalex.org/W2670151302","https://openalex.org/W2744228767","https://openalex.org/W2766143712","https://openalex.org/W2794141774","https://openalex.org/W2807127337","https://openalex.org/W2884201348","https://openalex.org/W2890289355","https://openalex.org/W2917909627","https://openalex.org/W2919115771","https://openalex.org/W4251575795","https://openalex.org/W4297775537","https://openalex.org/W6674914833","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2293118914","https://openalex.org/W2998381397","https://openalex.org/W4236419692","https://openalex.org/W3167919718","https://openalex.org/W4251718783","https://openalex.org/W2171015181","https://openalex.org/W4239447582","https://openalex.org/W1484403103","https://openalex.org/W2521947294","https://openalex.org/W2807127337"],"abstract_inverted_index":{"In":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs),":[4],"data":[5,77,89],"movement":[6],"inside":[7],"convolution":[8,22,39],"layers":[9],"between":[10],"memory":[11,31],"and":[12,33,68],"PEs":[13],"is":[14,84],"most":[15],"energy":[16,71],"dominant.":[17],"This":[18],"paper":[19],"proposes":[20],"a":[21],"processing":[23],"dataflow":[24],"that":[25],"reduces":[26],"both":[27],"the":[28,34,43,54,58,75,92],"number":[29],"of":[30],"accesses":[32],"on-chip":[35,48,66,95],"buffer":[36,67,96],"capacity":[37],"for":[38],"operations.":[40],"Based":[41],"on":[42],"dataflow,":[44],"we":[45],"design":[46],"an":[47],"buffer-minimized":[49],"CNN":[50,56,60],"accelerator.":[51],"Compared":[52],"with":[53,91],"state-of-the-art":[55],"accelerator,":[57],"proposed":[59,82],"accelerator":[61],"utilizes":[62],"2.30":[63],"times":[64,70],"less":[65],"2.18":[69],"efficiency":[72],"to":[73,86],"achieve":[74,87],"same":[76],"throughput":[78,90],"under":[79],"Alexnet.":[80],"The":[81],"architecture":[83],"able":[85],"higher":[88],"almost":[93],"constant":[94],"capacity.":[97]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}