{"id":"https://openalex.org/W3217204427","doi":"https://doi.org/10.1109/icce-tw52618.2021.9602953","title":"Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators","display_name":"Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators","publication_year":2021,"publication_date":"2021-09-15","ids":{"openalex":"https://openalex.org/W3217204427","doi":"https://doi.org/10.1109/icce-tw52618.2021.9602953","mag":"3217204427"},"language":"en","primary_location":{"id":"doi:10.1109/icce-tw52618.2021.9602953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9602953","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/A5088586836","display_name":"Shen\u2010Fu Hsiao","orcid":"https://orcid.org/0000-0002-4627-570X"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Shen-Fu Hsiao","raw_affiliation_strings":["National Sun Yat-Sen University, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Sun Yat-Sen University, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030410405","display_name":"Yu-Che Yen","orcid":null},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Che Yen","raw_affiliation_strings":["National Sun Yat-Sen University, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Sun Yat-Sen University, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088586836"],"corresponding_institution_ids":["https://openalex.org/I142974352"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16006536,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9977999925613403,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9970999956130981,"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/computation","display_name":"Computation","score":0.8674678206443787},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.8307770490646362},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7924268841743469},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6670087575912476},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4799407124519348},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4510743021965027},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4249490797519684},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42399922013282776},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.34570902585983276},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33742672204971313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30021560192108154}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.8674678206443787},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8307770490646362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7924268841743469},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6670087575912476},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4799407124519348},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4510743021965027},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4249490797519684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42399922013282776},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.34570902585983276},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33742672204971313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30021560192108154}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-tw52618.2021.9602953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9602953","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":[{"id":"https://openalex.org/F4320322108","display_name":"Ministry of Science and Technology","ror":"https://ror.org/032e49973"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2246760854","https://openalex.org/W2289252105","https://openalex.org/W2308155233","https://openalex.org/W2483966489","https://openalex.org/W2565960208","https://openalex.org/W2613779579","https://openalex.org/W2773339846","https://openalex.org/W2895531329","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6691194387"],"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/W3183118997","https://openalex.org/W3214410901","https://openalex.org/W3204400881","https://openalex.org/W3204296682","https://openalex.org/W2917767146"],"abstract_inverted_index":{"Quantization":[0],"of":[1,47],"parameters":[2,58],"and":[3,57],"activation":[4],"data":[5],"in":[6,16,49,69,73,80,99],"deep":[7],"neural":[8],"network":[9],"(DNN)":[10],"models":[11],"plays":[12],"an":[13,38],"important":[14],"role":[15],"multi-precision":[17,51,100],"DNN":[18,52,71,82,101],"hardware":[19,53,102],"accelerators":[20],"where":[21],"the":[22,31,44,50,60,77,81,88],"per-layer":[23],"bit-widths":[24],"are":[25],"dynamically":[26],"adjusted":[27],"to":[28,75],"speed":[29],"up":[30],"computation.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36,64],"present":[37],"efficient":[39,97],"quantization":[40,93],"algorithm":[41,90],"which":[42],"considers":[43],"supported":[45],"types":[46],"bit-width":[48],"when":[54],"quantizing":[55],"activations":[56],"from":[59],"pre-trained":[61],"models.":[62],"Furthermore,":[63],"consider":[65],"various":[66],"computation":[67,98],"complexity":[68],"different":[70],"layers":[72],"order":[74],"minimize":[76],"execution":[78],"time":[79],"hardware.":[83],"Experimental":[84],"results":[85,94],"show":[86],"that":[87],"proposed":[89],"has":[91],"better":[92],"for":[95],"more":[96],"accelerators.":[103]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
