{"id":"https://openalex.org/W2744809494","doi":null,"title":"Snowflake: A Model Agnostic Accelerator for Convolutional Neural Networks","display_name":"Snowflake: A Model Agnostic Accelerator for Convolutional Neural Networks","publication_year":2017,"publication_date":"2017-08-08","ids":{"openalex":"https://openalex.org/W2744809494","mag":"2744809494"},"language":"en","primary_location":{"id":"mag:2744809494","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1708.02579.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1708.02579.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056326889","display_name":"Vinayak Gokhale","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Vinayak Gokhale","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5056326889"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5996000170707703,"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":0.5996000170707703,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.36959999799728394,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.01510000042617321,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9269999861717224},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.8980000019073486},{"id":"https://openalex.org/keywords/data-access-patterns","display_name":"data access patterns","score":0.8159999847412109},{"id":"https://openalex.org/keywords/network-hierarchy","display_name":"network hierarchy","score":0.4860000014305115},{"id":"https://openalex.org/keywords/particle-accelerator-design","display_name":"accelerator design","score":0.3610000014305115},{"id":"https://openalex.org/keywords/deep-learning-hardware","display_name":"deep learning hardware","score":0.492000013589859}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8423285484313965},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7746756076812744},{"id":"https://openalex.org/C54059038","wikidata":"https://www.wikidata.org/wiki/Q550147","display_name":"Snowflake","level":3,"score":0.690331220626831},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5782807469367981},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5704287886619568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5473979115486145},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5389483571052551},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5141049027442932},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.503753125667572},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.45810791850090027},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.42792871594429016},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4193114638328552},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.41090887784957886},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.37175875902175903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3395612835884094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3172833323478699},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1030091643333435},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:2744809494","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1708.02579.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:docs.lib.purdue.edu:dissertations-19411","doi":null,"is_oa":false,"landing_page_url":"https://docs.lib.purdue.edu/dissertations/AAI10273425","pdf_url":null,"source":{"id":"https://openalex.org/S4377196310","display_name":"Purdue e-Pubs (Purdue University System)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801333002","host_organization_name":"Purdue University System","host_organization_lineage":["https://openalex.org/I2801333002"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Theses and Dissertations Available from ProQuest","raw_type":"text"}],"best_oa_location":{"id":"mag:2744809494","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1708.02579.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1598866093","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1841592590","https://openalex.org/W1903029394","https://openalex.org/W2044535169","https://openalex.org/W2067523571","https://openalex.org/W2094756095","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2152839228","https://openalex.org/W2274287116","https://openalex.org/W2276486856","https://openalex.org/W2285660444","https://openalex.org/W2289252105","https://openalex.org/W2520083297","https://openalex.org/W2558580397","https://openalex.org/W2582737986","https://openalex.org/W2585540825","https://openalex.org/W2949605076","https://openalex.org/W2949650786","https://openalex.org/W2963674932","https://openalex.org/W2964299589","https://openalex.org/W4394670654"],"related_works":["https://openalex.org/W4298065884","https://openalex.org/W2759398875","https://openalex.org/W2900207741","https://openalex.org/W2740539392","https://openalex.org/W2945393140","https://openalex.org/W2966818763","https://openalex.org/W2737762472","https://openalex.org/W2998032168","https://openalex.org/W2900391839","https://openalex.org/W7084137338"],"abstract_inverted_index":{"Deep":[0],"learning":[1,31],"is":[2,144],"becoming":[3],"increasingly":[4],"popular":[5],"for":[6,41,101,135,146],"a":[7,27,129,158,186,191,220,225],"wide":[8],"variety":[9,192],"of":[10,29,51,72,107,109,128,180,190,193,205,217,222,227],"applications":[11],"including":[12],"object":[13],"detection,":[14],"classification,":[15],"semantic":[16],"segmentation":[17],"and":[18,75,112,124,139,224],"natural":[19],"language":[20],"processing.":[21],"Convolutional":[22],"neural":[23],"networks":[24],"(CNNs)":[25],"are":[26,45],"class":[28],"deep":[30,195],"algorithms":[32],"that":[33,210],"have":[34,113],"been":[35,167],"shown":[36],"to":[37,53,62,78,149,152,169,172,201],"achieve":[38,150,170],"high":[39,85],"accuracy":[40],"these":[42,73,94],"tasks.":[43],"CNNs":[44,104,196],"hierarchical":[46],"mathematical":[47],"models":[48,95],"comprising":[49],"millions":[50],"operations":[52],"produce":[54,105],"an":[55,98,213],"output.":[56],"This":[57],"output":[58],"can":[59],"be":[60],"used":[61],"identify":[63],"what":[64,76],"objects":[65,74],"the":[66,70,90,126,181,203,206],"input":[67],"image":[68],"contained,":[69],"locations":[71],"actions":[77],"take":[79],"based":[80],"on":[81],"this":[82],"knowledge.":[83],"The":[84],"computational":[86,154,174,215],"complexity":[87],"combined":[88],"with":[89,197,219],"inherent":[91],"parallelism":[92],"in":[93],"makes":[96],"them":[97],"excellent":[99],"target":[100],"custom":[102,147],"accelerators.":[103],"tens":[106],"megabytes":[108],"intermediate":[110],"data":[111,116,140],"highly":[114],"varied":[115],"access":[117,141],"patterns,":[118,142],"both":[119],"among":[120],"different":[121,136,198],"network":[122,159],"architectures":[123],"across":[125],"hierarchy":[127],"single":[130],"network.":[131],"However,":[132],"when":[133],"optimizing":[134],"CNN":[137,182],"hierarchies":[138,200],"it":[143],"difficult":[145],"accelerators":[148],"close":[151,171],"100%":[153],"efficiency.":[155],"We":[156,184,208],"present":[157],"architecture":[160],"agnostic":[161],"accelerator":[162],"called":[163],"Snowflake.":[164],"Snowflake":[165,211],"has":[166,212],"designed":[168],"peak":[173],"efficiency":[175,216],"while":[176],"processing":[177],"all":[178],"parts":[179],"hierarchy.":[183],"use":[185],"benchmark":[187],"suite":[188],"consisting":[189],"recent,":[194],"model":[199],"evaluate":[202],"performance":[204],"accelerator.":[207],"demonstrate":[209],"average":[214],"93%":[218],"minimum":[221],"91%":[223],"maximum":[226],"95%.":[228]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2017-08-17T00:00:00"}