{"id":"https://openalex.org/W3216094497","doi":"https://doi.org/10.1109/niles53778.2021.9600528","title":"SNAPE-FP: SqueezeNet CNN with Accelerated Pooling Layers Extension based on IEEE-754 Floating Point Implementation through SW/HW Partitioning On ZYNQ SoC","display_name":"SNAPE-FP: SqueezeNet CNN with Accelerated Pooling Layers Extension based on IEEE-754 Floating Point Implementation through SW/HW Partitioning On ZYNQ SoC","publication_year":2021,"publication_date":"2021-10-23","ids":{"openalex":"https://openalex.org/W3216094497","doi":"https://doi.org/10.1109/niles53778.2021.9600528","mag":"3216094497"},"language":"en","primary_location":{"id":"doi:10.1109/niles53778.2021.9600528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/niles53778.2021.9600528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","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/A5046495109","display_name":"Abdelrhman M. Abotaleb","orcid":"https://orcid.org/0000-0001-6391-9960"},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Abdelrhman M. Abotaleb","raw_affiliation_strings":["Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090780442","display_name":"Mohab H. Ahmed","orcid":null},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mohab H. Ahmed","raw_affiliation_strings":["Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087174926","display_name":"Mazen A. Fathi","orcid":null},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mazen A. Fathi","raw_affiliation_strings":["Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046495109"],"corresponding_institution_ids":["https://openalex.org/I145487455"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50785948,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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":0.9998999834060669,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9973000288009644,"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":0.7983101010322571},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6221004128456116},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5672276020050049},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.5602533221244812},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.5142485499382019},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4360220432281494},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4246850609779358},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.42167624831199646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4150616228580475},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.34611836075782776},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3303956985473633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30855393409729004},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20184442400932312},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0849059522151947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7983101010322571},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6221004128456116},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5672276020050049},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.5602533221244812},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.5142485499382019},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4360220432281494},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4246850609779358},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.42167624831199646},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4150616228580475},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.34611836075782776},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3303956985473633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30855393409729004},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20184442400932312},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0849059522151947},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/niles53778.2021.9600528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/niles53778.2021.9600528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2279098554","https://openalex.org/W2527036487","https://openalex.org/W2618530766","https://openalex.org/W2795751400","https://openalex.org/W2801550278","https://openalex.org/W2911374887","https://openalex.org/W2935524202","https://openalex.org/W2954065928","https://openalex.org/W2963256077","https://openalex.org/W2982519304","https://openalex.org/W3011049621","https://openalex.org/W3043044535","https://openalex.org/W3126868115","https://openalex.org/W6695314431","https://openalex.org/W6749906607","https://openalex.org/W6922837747"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2027972911","https://openalex.org/W2146343568","https://openalex.org/W2013643406","https://openalex.org/W2157978810","https://openalex.org/W3028347934","https://openalex.org/W2185692674"],"abstract_inverted_index":{"It":[0],"is":[1,98,122,178,184],"clearly":[2],"known":[3],"that":[4,32],"deep":[5,23],"learning":[6,24],"applications":[7],"are":[8,50,71,80],"enormously":[9],"used":[10],"in":[11,158],"the":[12,40,84,107,111,116,120,141,151,171,176,179,188],"image":[13,19],"classification,":[14],"object":[15],"tracking":[16],"and":[17,77,115,148,175],"related":[18],"analysis":[20],"techniques.":[21],"But":[22],"networks":[25],"usually":[26],"involve":[27],"huge":[28,55,67],"number":[29],"of":[30,89,127],"parameters":[31],"need":[33],"to":[34,38,52,58,133],"be":[35,59],"extensively":[36],"processed":[37],"produce":[39],"classification":[41],"output,":[42],"which":[43,183],"also":[44,78],"takes":[45],"a":[46],"considerable":[47],"time.":[48,63],"GPUs":[49,65],"exploited":[51],"do":[53],"such":[54],"parallel":[56,125],"computations":[57],"finished":[60],"within":[61],"acceptable":[62],"Still":[64],"consume":[66],"power,":[68],"so":[69],"they":[70,79],"not":[72],"suitable":[73],"for":[74,140,150,170],"embedded":[75],"solutions,":[76],"very":[81],"expensive.":[82],"In":[83],"current":[85],"work,":[86],"complete":[87],"implementation":[88,108],"floating":[90],"point":[91],"based":[92],"SqueezeNet":[93],"convolutional":[94],"neural":[95],"network":[96],"(CNN)":[97],"done":[99,123],"on":[100,109,131],"ZYNQ":[101],"System-On-Chip":[102],"(SoC)":[103],"XC7020":[104],"via":[105,124],"partitioning":[106],"both":[110],"software":[112],"part":[113,118],"(ARM)":[114],"FPGA":[117],"(Artix-7),":[119],"acceleration":[121],"implementations":[126],"average":[128],"pool":[129],"layer":[130,144,154],"up":[132],"3":[134,156],"channels":[135,157],"with":[136],"speedup":[137],"=":[138],"6.37":[139],"Max":[142],"Pool":[143,153],"accelerated":[145,155],"single":[146],"channel":[147],"13.88":[149],"Average":[152],"parallel.":[159],"The":[160],"maximum":[161],"power":[162,173,181,190],"consumption":[163,182,191],"equals":[164],"1.549":[165],"watt":[166,169],"(only":[167],"0.136":[168],"static":[172],"consumption)":[174],"remaining":[177],"dynamic":[180],"greatly":[185],"less":[186],"than":[187],"GPU":[189],"(reaches":[192],"~":[193],"60":[194],"watt).":[195]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
