{"id":"https://openalex.org/W3211664117","doi":"https://doi.org/10.1109/dac18074.2021.9586311","title":"Scaling Deep-Learning Inference with Chiplet-based Architecture and Photonic Interconnects","display_name":"Scaling Deep-Learning Inference with Chiplet-based Architecture and Photonic Interconnects","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3211664117","doi":"https://doi.org/10.1109/dac18074.2021.9586311","mag":"3211664117"},"language":"en","primary_location":{"id":"doi:10.1109/dac18074.2021.9586311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","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/A5101512989","display_name":"Yuan Li","orcid":"https://orcid.org/0000-0003-3428-8340"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuan Li","raw_affiliation_strings":["George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034189643","display_name":"Ahmed Louri","orcid":"https://orcid.org/0000-0003-4262-6688"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Louri","raw_affiliation_strings":["George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014895124","display_name":"Avinash Karanth","orcid":"https://orcid.org/0000-0002-9472-4637"},"institutions":[{"id":"https://openalex.org/I4210106879","display_name":"Ohio University","ror":"https://ror.org/01jr3y717","country_code":"US","type":"education","lineage":["https://openalex.org/I4210106879"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avinash Karanth","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA","institution_ids":["https://openalex.org/I4210106879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101512989"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":2.3116,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.90422097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"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/T10299","display_name":"Photonic and Optical Devices","score":0.9984999895095825,"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":0.7814050912857056},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6068482398986816},{"id":"https://openalex.org/keywords/photonics","display_name":"Photonics","score":0.5302574038505554},{"id":"https://openalex.org/keywords/infiniband","display_name":"InfiniBand","score":0.5120142102241516},{"id":"https://openalex.org/keywords/dataflow","display_name":"Dataflow","score":0.49466750025749207},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4567069709300995},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4505874514579773},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.36752185225486755},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.2559341788291931},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09659361839294434}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7814050912857056},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6068482398986816},{"id":"https://openalex.org/C20788544","wikidata":"https://www.wikidata.org/wiki/Q467054","display_name":"Photonics","level":2,"score":0.5302574038505554},{"id":"https://openalex.org/C2781030343","wikidata":"https://www.wikidata.org/wiki/Q922437","display_name":"InfiniBand","level":2,"score":0.5120142102241516},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.49466750025749207},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4567069709300995},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4505874514579773},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.36752185225486755},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2559341788291931},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09659361839294434},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac18074.2021.9586311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","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":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1985818188","https://openalex.org/W2069387756","https://openalex.org/W2092535517","https://openalex.org/W2097117768","https://openalex.org/W2116036846","https://openalex.org/W2135129294","https://openalex.org/W2141391051","https://openalex.org/W2162639668","https://openalex.org/W2194775991","https://openalex.org/W2216869242","https://openalex.org/W2343329434","https://openalex.org/W2464177207","https://openalex.org/W2588191434","https://openalex.org/W2595633882","https://openalex.org/W2604319603","https://openalex.org/W2612406526","https://openalex.org/W2940862705","https://openalex.org/W2945773942","https://openalex.org/W2980104813","https://openalex.org/W2987711821","https://openalex.org/W3004495293","https://openalex.org/W3035792034","https://openalex.org/W3042495273","https://openalex.org/W3097964456","https://openalex.org/W6667672563","https://openalex.org/W6677135797","https://openalex.org/W6719768283","https://openalex.org/W6737414912"],"related_works":["https://openalex.org/W2406331183","https://openalex.org/W2363988059","https://openalex.org/W1939358748","https://openalex.org/W2053215237","https://openalex.org/W2792231649","https://openalex.org/W1995426833","https://openalex.org/W2613555492","https://openalex.org/W3018923871","https://openalex.org/W1998888015","https://openalex.org/W3046837943"],"abstract_inverted_index":{"Chiplet-based":[0],"architectures":[1,179],"have":[2],"been":[3],"proposed":[4],"to":[5,34,99,147,175],"scale":[6],"computing":[7],"systems":[8],"for":[9,19,91],"deep":[10],"neural":[11],"networks":[12],"(DNNs).":[13],"Prior":[14],"work":[15],"has":[16],"shown":[17],"that":[18,75,118,135,160],"the":[20,24,28,52,101,137],"chiplet-based":[21,87,178],"DNN":[22,79,92,124,151,157],"accelerators,":[23],"electrical":[25,55,181],"network":[26,117],"connecting":[27],"chiplets":[29],"poses":[30],"a":[31,86,96,114,132],"major":[32],"challenge":[33],"system":[35],"performance,":[36],"energy":[37,65,169],"consumption,":[38],"and":[39,67,72,106,108,130,141,164,168],"scalability.":[40],"Some":[41],"emerging":[42],"interconnect":[43],"technologies":[44],"such":[45],"as":[46,57,173],"silicon":[47],"photonics":[48],"can":[49,119],"potentially":[50],"overcome":[51],"challenges":[53],"facing":[54],"interconnects":[56,59,146],"photonic":[58,116,145,183],"provide":[60],"high":[61],"bandwidth":[62],"density,":[63],"superior":[64],"efficiency,":[66],"ease":[68,138],"of":[69,139,144],"implementing":[70],"broadcast":[71,140],"multicast":[73,142],"operations":[74],"are":[76],"prevalent":[77],"in":[78,123],"inference.":[80,93],"In":[81],"this":[82],"paper,":[83],"we":[84],"propose":[85],"architecture":[88],"named":[89],"SPRINT":[90,94,161],"uses":[95],"global":[97],"buffer":[98],"simplify":[100],"data":[102],"transmission":[103],"between":[104],"storage":[105],"computation,":[107],"includes":[109],"two":[110],"novel":[111],"designs:":[112],"(1)":[113],"reconfigurable":[115],"support":[120,148],"diverse":[121],"communications":[122],"inference":[125],"with":[126,180],"minimal":[127],"implementation":[128],"cost,":[129],"(2)":[131],"customized":[133],"dataflow":[134],"exploits":[136],"feature":[143],"highly":[149],"parallel":[150],"computations.":[152],"Simulation":[153],"studies":[154],"using":[155],"ResNet50":[156],"model":[158],"show":[159],"achieves":[162],"46%":[163],"61%":[165],"execution":[166],"time":[167],"consumption":[170],"reduction,":[171],"respectively,":[172],"compared":[174],"other":[176],"state-of-the-art":[177],"or":[182],"interconnects.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}