{"id":"https://openalex.org/W3215427995","doi":"https://doi.org/10.1109/icce-tw52618.2021.9603260","title":"Hardware Generator for Edge Artificial Intelligent Inference System","display_name":"Hardware Generator for Edge Artificial Intelligent Inference System","publication_year":2021,"publication_date":"2021-09-15","ids":{"openalex":"https://openalex.org/W3215427995","doi":"https://doi.org/10.1109/icce-tw52618.2021.9603260","mag":"3215427995"},"language":"en","primary_location":{"id":"doi:10.1109/icce-tw52618.2021.9603260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9603260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","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/A5008507157","display_name":"Yeong-Kang Lai","orcid":"https://orcid.org/0000-0002-5465-7935"},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yeong-Kang Lai","raw_affiliation_strings":["National Chung Hsing University, Taichung City, Taiwan (R.O.C.)"],"affiliations":[{"raw_affiliation_string":"National Chung Hsing University, Taichung City, Taiwan (R.O.C.)","institution_ids":["https://openalex.org/I162838928"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066707600","display_name":"Chuan-Wei Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chuan-Wei Huang","raw_affiliation_strings":["National Chung Hsing University, Taichung City, Taiwan (R.O.C.)"],"affiliations":[{"raw_affiliation_string":"National Chung Hsing University, Taichung City, Taiwan (R.O.C.)","institution_ids":["https://openalex.org/I162838928"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008507157"],"corresponding_institution_ids":["https://openalex.org/I162838928"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14317462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9829999804496765,"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"}},"topics":[{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9829999804496765,"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/T10320","display_name":"Neural Networks and Applications","score":0.9714999794960022,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9430999755859375,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.8305205702781677},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.7133278846740723},{"id":"https://openalex.org/keywords/verilog","display_name":"Verilog","score":0.6901232004165649},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4862576723098755},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48601800203323364},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4762585163116455},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.4634738862514496},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.4594254493713379},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.44691357016563416},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.44378262758255005},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.42332208156585693},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.3949354588985443},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.17961949110031128},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16884008049964905},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.0736193060874939}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8305205702781677},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.7133278846740723},{"id":"https://openalex.org/C2779030575","wikidata":"https://www.wikidata.org/wiki/Q827773","display_name":"Verilog","level":3,"score":0.6901232004165649},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4862576723098755},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48601800203323364},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4762585163116455},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4634738862514496},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4594254493713379},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.44691357016563416},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.44378262758255005},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.42332208156585693},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.3949354588985443},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.17961949110031128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16884008049964905},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0736193060874939},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C129307140","wikidata":"https://www.wikidata.org/wiki/Q6795880","display_name":"Maximum bubble pressure method","level":3,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C157915830","wikidata":"https://www.wikidata.org/wiki/Q2928001","display_name":"Bubble","level":2,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/icce-tw52618.2021.9603260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9603260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2616318524","https://openalex.org/W2625954420","https://openalex.org/W2942820769","https://openalex.org/W3003453708","https://openalex.org/W6738295366"],"related_works":["https://openalex.org/W2387264083","https://openalex.org/W4225949190","https://openalex.org/W2604877941","https://openalex.org/W1669114967","https://openalex.org/W2605479516","https://openalex.org/W2390885485","https://openalex.org/W2362523726","https://openalex.org/W1534855236","https://openalex.org/W1761969858","https://openalex.org/W1967938402"],"abstract_inverted_index":{"This":[0],"paper":[1],"designs":[2],"an":[3],"optimized":[4,47],"hardware":[5,22],"generator":[6,37],"(IP":[7],"Generator)":[8],"based":[9],"on":[10,68],"convolutional":[11],"neural":[12,25],"networks.":[13],"Users":[14],"can":[15,38,43,65,73],"use":[16],"IP":[17,36],"Generator":[18],"to":[19],"create":[20],"any":[21],"architectures":[23],"for":[24],"network":[26,52,55,60],"model":[27,56],"they":[28],"want.":[29],"By":[30],"the":[31,35,40,45],"efficient":[32],"user":[33],"interface,":[34],"output":[39],"architecture.":[41],"You":[42],"get":[44],"corresponding":[46],"Verilog":[48],"code":[49],"efficiently.":[50],"The":[51],"is":[53],"a":[54],"that":[57],"simplifies":[58],"some":[59],"layers":[61],"by":[62],"Yolo-v1.":[63],"It":[64],"run":[66],"100MHz":[67],"Xilinx":[69],"ZCU102":[70],"board":[71],"which":[72],"reach":[74],"28.8GOP/s.":[75]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}