{"id":"https://openalex.org/W2625273935","doi":"https://doi.org/10.1145/3061639.3072944","title":"Accelerator Design for Deep Learning Training","display_name":"Accelerator Design for Deep Learning Training","publication_year":2017,"publication_date":"2017-06-13","ids":{"openalex":"https://openalex.org/W2625273935","doi":"https://doi.org/10.1145/3061639.3072944","mag":"2625273935"},"language":"en","primary_location":{"id":"doi:10.1145/3061639.3072944","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3061639.3072944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th Annual Design Automation Conference 2017","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/A5103063911","display_name":"Ankur Agrawal","orcid":"https://orcid.org/0000-0002-4389-5911"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ankur Agrawal","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101406569","display_name":"Chia\u2010Yu Chen","orcid":"https://orcid.org/0000-0001-5542-7149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chia-Yu Chen","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078440061","display_name":"Jungwook Choi","orcid":"https://orcid.org/0000-0002-3075-8694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jungwook Choi","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101429456","display_name":"Kailash Gopalakrishnan","orcid":"https://orcid.org/0000-0002-8952-0875"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kailash Gopalakrishnan","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089478486","display_name":"Jinwook Oh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinwook Oh","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012408072","display_name":"Sunil Shukla","orcid":"https://orcid.org/0000-0002-9268-4096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sunil Shukla","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101784873","display_name":"V. Srinivasan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Viji Srinivasan","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010094713","display_name":"Swagath Venkataramani","orcid":"https://orcid.org/0000-0002-0470-6364"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swagath Venkataramani","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103019753","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0002-6076-6425"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5103063911"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.62716489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9864000082015991,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9821000099182129,"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":0.8093492984771729},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.756756067276001},{"id":"https://openalex.org/keywords/pace","display_name":"Pace","score":0.6606266498565674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6423115134239197},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5067899823188782},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.48659002780914307},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.47645214200019836},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47097304463386536},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4310440719127655},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.41354793310165405},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3597528636455536},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12902262806892395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8093492984771729},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.756756067276001},{"id":"https://openalex.org/C2777526511","wikidata":"https://www.wikidata.org/wiki/Q691543","display_name":"Pace","level":2,"score":0.6606266498565674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6423115134239197},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5067899823188782},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.48659002780914307},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.47645214200019836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47097304463386536},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4310440719127655},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.41354793310165405},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3597528636455536},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12902262806892395},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"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/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3061639.3072944","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3061639.3072944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th Annual Design Automation Conference 2017","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":0.8700000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1841592590","https://openalex.org/W2553417306","https://openalex.org/W2568564101","https://openalex.org/W2963374099","https://openalex.org/W2963964896","https://openalex.org/W4243157141"],"related_works":["https://openalex.org/W2386723501","https://openalex.org/W2387879414","https://openalex.org/W2390304029","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843","https://openalex.org/W3208304128"],"abstract_inverted_index":{"Deep":[0,99],"Neural":[1],"Networks":[2,100],"(DNNs)":[3],"have":[4,42],"emerged":[5],"as":[6,26,46],"a":[7,119],"powerful":[8],"and":[9,36,39,75,112,131],"versatile":[10],"set":[11],"of":[12,67,69,73,83,96,110],"techniques":[13],"showing":[14],"successes":[15],"on":[16,101],"challenging":[17],"artificial":[18],"intelligence":[19],"(AI)":[20],"problems.":[21],"Applications":[22],"in":[23,126],"domains":[24],"such":[25],"image/video":[27],"processing,":[28,33],"autonomous":[29],"cars,":[30],"natural":[31],"language":[32],"speech":[34],"synthesis":[35],"recognition,":[37],"genomics":[38],"many":[40],"others":[41],"embraced":[43],"deep":[44],"learning":[45],"the":[47,97,108,124,129,132],"foundation.":[48],"DNNs":[49],"achieve":[50],"superior":[51],"accuracy":[52],"for":[53,78,136],"these":[54,84],"applications":[55],"with":[56],"high":[57,76],"computational":[58],"complexity":[59],"using":[60],"very":[61],"large":[62,102],"models":[63],"which":[64,105],"require":[65],"100s":[66],"MBs":[68],"data":[70,79],"storage,":[71],"exaops":[72],"computation":[74],"bandwidth":[77],"movement.":[80],"In":[81,114],"spite":[82],"impressive":[85],"advances,":[86],"it":[87],"still":[88],"takes":[89],"days":[90],"to":[91,93,122],"weeks":[92],"train":[94],"state":[95],"art":[98],"datasets":[103],"-":[104],"directly":[106],"limits":[107],"pace":[109],"innovation":[111],"adoption.":[113],"this":[115],"paper,":[116],"we":[117],"present":[118],"multi-pronged":[120],"approach":[121],"address":[123],"challenges":[125],"meeting":[127],"both":[128],"throughput":[130],"energy":[133],"efficiency":[134],"goals":[135],"DNN":[137],"training.":[138]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
