{"id":"https://openalex.org/W2920193640","doi":null,"title":"Caffeinated FPGAs: FPGA Framework for Training and Inference of Convolutional Neural Networks With Reduced Precision Floating-Point Arithmetic","display_name":"Caffeinated FPGAs: FPGA Framework for Training and Inference of Convolutional Neural Networks With Reduced Precision Floating-Point Arithmetic","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2920193640","mag":"2920193640"},"language":"en","primary_location":{"id":"pmh:oai:utoronto.scholaris.ca:1807/89598","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1807/89598","pdf_url":"https://utoronto.scholaris.ca/bitstreams/a7a8e343-55b9-412a-ad11-e69b797d3ae6/download","source":{"id":"https://openalex.org/S4306400261","display_name":"TSpace (University of Toronto)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I185261750","host_organization_name":"University of Toronto","host_organization_lineage":["https://openalex.org/I185261750"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://utoronto.scholaris.ca/bitstreams/a7a8e343-55b9-412a-ad11-e69b797d3ae6/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015845004","display_name":"Roberto DiCecco","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Roberto DiCecco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5015845004"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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/T11697","display_name":"Numerical Methods and Algorithms","score":0.8403000235557556,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11697","display_name":"Numerical Methods and Algorithms","score":0.8403000235557556,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.07620000094175339,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.06809999793767929,"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/fpga","display_name":"FPGA","score":0.7900000214576721},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9309999942779541},{"id":"https://openalex.org/keywords/high-level-synthesis","display_name":"high-level synthesis","score":0.6859999895095825},{"id":"https://openalex.org/keywords/neural-network-inference","display_name":"neural network inference","score":0.6499999761581421},{"id":"https://openalex.org/keywords/precision-requirements","display_name":"precision requirements","score":0.4699999988079071},{"id":"https://openalex.org/keywords/rounding-modes","display_name":"rounding modes","score":0.5789999961853027},{"id":"https://openalex.org/keywords/open-source-framework","display_name":"open source software framework","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.8656806349754333},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7500773668289185},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6261895895004272},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.6088098287582397},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5998040437698364},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5171267986297607},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.5009303092956543},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4281115233898163},{"id":"https://openalex.org/C35912277","wikidata":"https://www.wikidata.org/wiki/Q1243369","display_name":"Double-precision floating-point format","level":3,"score":0.41852816939353943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38821840286254883},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.38408318161964417},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.2922586500644684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2525103688240051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18953880667686462},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:utoronto.scholaris.ca:1807/89598","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1807/89598","pdf_url":"https://utoronto.scholaris.ca/bitstreams/a7a8e343-55b9-412a-ad11-e69b797d3ae6/download","source":{"id":"https://openalex.org/S4306400261","display_name":"TSpace (University of Toronto)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I185261750","host_organization_name":"University of Toronto","host_organization_lineage":["https://openalex.org/I185261750"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"mag:2920193640","doi":null,"is_oa":false,"landing_page_url":"https://tspace.library.utoronto.ca/bitstream/1807/89598/3/DiCecco_Roberto_201806_MAS_thesis.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:utoronto.scholaris.ca:1807/89598","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1807/89598","pdf_url":"https://utoronto.scholaris.ca/bitstreams/a7a8e343-55b9-412a-ad11-e69b797d3ae6/download","source":{"id":"https://openalex.org/S4306400261","display_name":"TSpace (University of Toronto)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I185261750","host_organization_name":"University of Toronto","host_organization_lineage":["https://openalex.org/I185261750"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2920193640.pdf","grobid_xml":"https://content.openalex.org/works/W2920193640.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1487564550","https://openalex.org/W1548328233","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W2018055497","https://openalex.org/W2028499920","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2123045220","https://openalex.org/W2172654076","https://openalex.org/W2194775991","https://openalex.org/W2343695530","https://openalex.org/W2483618896","https://openalex.org/W2513383847","https://openalex.org/W2520083297","https://openalex.org/W2528015417","https://openalex.org/W2542189141","https://openalex.org/W2574797063","https://openalex.org/W2584311934","https://openalex.org/W2585774018","https://openalex.org/W2647836899","https://openalex.org/W2919115771","https://openalex.org/W2949117887","https://openalex.org/W2950769435","https://openalex.org/W2963374099","https://openalex.org/W2963542991","https://openalex.org/W2964121744","https://openalex.org/W3021085108","https://openalex.org/W3118608800","https://openalex.org/W7098642242"],"related_works":["https://openalex.org/W2786618695","https://openalex.org/W3008072732","https://openalex.org/W2944640508","https://openalex.org/W2891946740","https://openalex.org/W2528015417","https://openalex.org/W3003453708","https://openalex.org/W4415332820","https://openalex.org/W4240275855","https://openalex.org/W7203867451","https://openalex.org/W2950896714"],"abstract_inverted_index":{"This":[0,20,51],"thesis":[1],"presents":[2],"a":[3,25,58,63,89,100,113,127],"framework":[4,103],"for":[5,27,139],"performing":[6],"training":[7,45,140],"and":[8,29,75,82,120,132,146],"inference":[9],"of":[10,37,44,57,116,130,135],"Convolutional":[11],"Neural":[12],"Networks":[13],"(CNNs)":[14],"with":[15,47,62,78,99],"reduced":[16],"precision":[17,42],"floating-point":[18],"arithmetic.":[19],"work":[21],"aims":[22],"to":[23,33,39,124],"provide":[24],"means":[26],"FPGA":[28,90,101,104],"machine":[30],"learning":[31],"researchers":[32],"use":[34],"the":[35,41,55,144],"customizability":[36],"FPGAs":[38],"explore":[40],"requirements":[43],"CNNs":[46],"an":[48],"open-source":[49],"framework.":[50],"is":[52,70,107,137],"accomplished":[53],"through":[54],"creation":[56],"High-Level":[59],"Synthesis":[60],"library":[61,88],"Custom":[64],"Precision":[65],"Floating-Point":[66],"data":[67],"type":[68],"that":[69,126],"configurable":[71],"in":[72],"both":[73],"exponent":[74,133],"mantissa":[76,128],"widths,":[77],"several":[79,141],"standard":[80],"operators":[81],"rounding":[83],"modes":[84],"supported.":[85],"With":[86],"this":[87],"CNN":[91,102],"Training":[92],"Engine":[93],"(FCTE)":[94],"has":[95,112,121],"been":[96,122],"created":[97],"along":[98],"Caffe,":[105],"which":[106],"built":[108],"on":[109],"Caffe.":[110],"FCTE":[111],"peak":[114],"performance":[115],"approximately":[117],"350":[118],"GFLOPs,":[119],"used":[123],"show":[125],"width":[129,134],"5":[131],"6":[136],"sufficient":[138],"models":[142],"targeting":[143],"MNIST":[145],"CIFAR-10":[147],"datasets.":[148]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-03-11T00:00:00"}