{"id":"https://openalex.org/W3006097798","doi":"https://doi.org/10.1109/uemcon47517.2019.8992928","title":"A SS-CNN on an FPGA for Handwritten Digit Recognition","display_name":"A SS-CNN on an FPGA for Handwritten Digit Recognition","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3006097798","doi":"https://doi.org/10.1109/uemcon47517.2019.8992928","mag":"3006097798"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8992928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8992928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5011676423","display_name":"Jiong Si","orcid":null},"institutions":[{"id":"https://openalex.org/I133999245","display_name":"University of Nevada, Las Vegas","ror":"https://ror.org/0406gha72","country_code":"US","type":"education","lineage":["https://openalex.org/I133999245"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiong Si","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A","institution_ids":["https://openalex.org/I133999245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074949923","display_name":"Evangelos A. Yfantis","orcid":null},"institutions":[{"id":"https://openalex.org/I133999245","display_name":"University of Nevada, Las Vegas","ror":"https://ror.org/0406gha72","country_code":"US","type":"education","lineage":["https://openalex.org/I133999245"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evangelos Yfantis","raw_affiliation_strings":["Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A","institution_ids":["https://openalex.org/I133999245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065027413","display_name":"Sarah Harris","orcid":"https://orcid.org/0000-0002-2807-9529"},"institutions":[{"id":"https://openalex.org/I133999245","display_name":"University of Nevada, Las Vegas","ror":"https://ror.org/0406gha72","country_code":"US","type":"education","lineage":["https://openalex.org/I133999245"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarah L. Harris","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, U.S.A","institution_ids":["https://openalex.org/I133999245"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133999245"],"apc_list":null,"apc_paid":null,"fwci":0.4063,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.67374189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"0088","last_page":"0093"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9772999882698059,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9696000218391418,"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/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.8951008319854736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7964924573898315},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7526485919952393},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.5621645450592041},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5345795154571533},{"id":"https://openalex.org/keywords/cyclone","display_name":"Cyclone (programming language)","score":0.4653051495552063},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.46039173007011414},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.4323931634426117},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4274842441082001},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42412081360816956},{"id":"https://openalex.org/keywords/digit-recognition","display_name":"Digit recognition","score":0.41529178619384766},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.365123450756073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32553690671920776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16482889652252197}],"concepts":[{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.8951008319854736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964924573898315},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7526485919952393},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.5621645450592041},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5345795154571533},{"id":"https://openalex.org/C2777864850","wikidata":"https://www.wikidata.org/wiki/Q79598","display_name":"Cyclone (programming language)","level":3,"score":0.4653051495552063},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.46039173007011414},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4323931634426117},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4274842441082001},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42412081360816956},{"id":"https://openalex.org/C2984784707","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Digit recognition","level":3,"score":0.41529178619384766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.365123450756073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32553690671920776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16482889652252197},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8992928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8992928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1973695593","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2119144962","https://openalex.org/W2163605009","https://openalex.org/W2789461907","https://openalex.org/W2805003733","https://openalex.org/W2896006880","https://openalex.org/W2912776959","https://openalex.org/W2962835968","https://openalex.org/W2963813662","https://openalex.org/W2964299589","https://openalex.org/W6637373629","https://openalex.org/W6674914833","https://openalex.org/W6677580257","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W3093612317","https://openalex.org/W2613736958","https://openalex.org/W2067951144","https://openalex.org/W2175746458","https://openalex.org/W2732542196","https://openalex.org/W2760085659","https://openalex.org/W2883200793","https://openalex.org/W2738221750","https://openalex.org/W3012978760","https://openalex.org/W2366084615"],"abstract_inverted_index":{"This":[0,25],"paper":[1],"describes":[2],"a":[3,13,90,108],"Super-Skinny":[4],"Convolutional":[5],"Neural":[6],"Network":[7],"(SS-CNN)":[8],"and":[9,35,80,115],"its":[10],"implementation":[11],"on":[12],"Cyclone":[14],"IVE":[15],"field":[16],"programmable":[17],"gate":[18],"array":[19],"(FPGA),":[20],"for":[21],"handwritten":[22],"digit":[23],"recognition.":[24],"SS-CNN":[26,50,106],"performs":[27],"state-of-the-art":[28],"recognition":[29,53,69],"accuracy":[30,54],"but":[31],"with":[32,40],"fewer":[33],"layers":[34],"less":[36],"neurons.":[37],"Using":[38],"parameters":[39],"8":[41],"bits":[42],"of":[43,48,72],"precision,":[44],"the":[45,59,73,101],"FPGA":[46,75,83],"solutions":[47,76,88],"this":[49],"show":[51],"no":[52],"loss":[55],"when":[56,98],"compared":[57,99],"to":[58,67,92,100],"32-bit":[60],"floating":[61],"point":[62],"software":[63,102],"solution.":[64,103,118],"In":[65],"addition":[66],"high":[68],"accuracy,":[70],"both":[71],"proposed":[74,86],"are":[77],"low":[78,110],"power":[79,95,116],"require":[81],"little":[82],"area.":[84],"The":[85],"hardware":[87,113],"indicate":[89],"67":[91],"355":[93],"times":[94],"savings":[96],"potential":[97],"Thus,":[104],"our":[105],"provides":[107],"high-performance,":[109],"computation":[111],"demands,":[112],"friendly,":[114],"efficient":[117]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}