{"id":"https://openalex.org/W3136275618","doi":"https://doi.org/10.1109/lsp.2021.3067188","title":"ATA: Attentional Non-Linear Activation Function Approximation for VLSI-Based Neural Networks","display_name":"ATA: Attentional Non-Linear Activation Function Approximation for VLSI-Based Neural Networks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3136275618","doi":"https://doi.org/10.1109/lsp.2021.3067188","mag":"3136275618"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3067188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3067188","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5101946370","display_name":"Linyu Wei","orcid":"https://orcid.org/0000-0003-4773-2228"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linyu Wei","raw_affiliation_strings":["State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4773-2228","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101425172","display_name":"Jueping Cai","orcid":"https://orcid.org/0000-0002-1852-0982"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jueping Cai","raw_affiliation_strings":["State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-1852-0982","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061097185","display_name":"Wuzhuang Wang","orcid":"https://orcid.org/0000-0001-5923-2381"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wuzhuang Wang","raw_affiliation_strings":["State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-5923-2381","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Wide Bandgap Semiconductor Technology Disciplines, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.6595,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.69579438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"28","issue":null,"first_page":"793","last_page":"797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/activation-function","display_name":"Activation function","score":0.8000959157943726},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.7652792930603027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7453509569168091},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7150481343269348},{"id":"https://openalex.org/keywords/function-approximation","display_name":"Function approximation","score":0.6686936616897583},{"id":"https://openalex.org/keywords/piecewise-linear-function","display_name":"Piecewise linear function","score":0.6166055798530579},{"id":"https://openalex.org/keywords/linear-approximation","display_name":"Linear approximation","score":0.5132830142974854},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.49051111936569214},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4691931903362274},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.45735234022140503},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4314141273498535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42449259757995605},{"id":"https://openalex.org/keywords/approximation-theory","display_name":"Approximation theory","score":0.41002801060676575},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.19830968976020813},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1720043122768402}],"concepts":[{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.8000959157943726},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.7652792930603027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7453509569168091},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7150481343269348},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.6686936616897583},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.6166055798530579},{"id":"https://openalex.org/C160824197","wikidata":"https://www.wikidata.org/wiki/Q2071054","display_name":"Linear approximation","level":3,"score":0.5132830142974854},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.49051111936569214},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4691931903362274},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.45735234022140503},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4314141273498535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42449259757995605},{"id":"https://openalex.org/C145242015","wikidata":"https://www.wikidata.org/wiki/Q774123","display_name":"Approximation theory","level":2,"score":0.41002801060676575},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.19830968976020813},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1720043122768402},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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/lsp.2021.3067188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3067188","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1632114991","https://openalex.org/W1686810756","https://openalex.org/W1978262091","https://openalex.org/W2000956074","https://openalex.org/W2074786446","https://openalex.org/W2091085210","https://openalex.org/W2130073327","https://openalex.org/W2167425979","https://openalex.org/W2169621283","https://openalex.org/W2525332836","https://openalex.org/W2890165800","https://openalex.org/W2909136751","https://openalex.org/W2955058313","https://openalex.org/W2963285578","https://openalex.org/W2963403868","https://openalex.org/W2964116539","https://openalex.org/W3011818954","https://openalex.org/W3023286399","https://openalex.org/W3024437012","https://openalex.org/W3025341614","https://openalex.org/W3048717007","https://openalex.org/W3086273127","https://openalex.org/W4289076097","https://openalex.org/W4298422451","https://openalex.org/W4385245566","https://openalex.org/W6637373629","https://openalex.org/W6669549402","https://openalex.org/W6685562342","https://openalex.org/W6727099177","https://openalex.org/W6739901393","https://openalex.org/W6757384037"],"related_works":["https://openalex.org/W2171221472","https://openalex.org/W2498835000","https://openalex.org/W1913954093","https://openalex.org/W2108297731","https://openalex.org/W2167394514","https://openalex.org/W2766569526","https://openalex.org/W3177870706","https://openalex.org/W3113145869","https://openalex.org/W1963832283","https://openalex.org/W2096450101"],"abstract_inverted_index":{"In":[0],"this":[1],"letter,":[2],"we":[3],"present":[4],"an":[5],"attentional":[6],"non-linear":[7],"activation":[8,60,67],"function":[9,61,86],"approximation":[10,20,115],"method":[11],"called":[12],"ATA":[13,35,111],"for":[14,73],"VLSI-based":[15,74],"neural":[16,75],"networks.":[17,76],"Unlike":[18],"other":[19,113],"methods":[21,116],"that":[22,109],"pursue":[23],"the":[24,34,37,43,48,59,65,83,96,102,110],"low":[25,92],"hardware":[26],"resources":[27],"with":[28,69,91,101],"a":[29],"high":[30],"recognition":[31,49,118],"accuracy":[32,50],"loss,":[33],"utilizes":[36],"pixel":[38],"attention":[39,56],"to":[40,46],"focus":[41],"on":[42],"important":[44,78],"features":[45,79,98],"keep":[47],"and":[51,87,121],"reduce":[52],"resource":[53],"cost.":[54],"Specifically,":[55],"applied":[57],"in":[58,117],"is":[62],"realized":[63],"by":[64,82],"approximated":[66],"functions":[68],"different":[70],"fitting":[71,93,104],"errors":[72],"The":[77],"are":[80,99],"highlighted":[81],"piecewise":[84],"linear":[85],"improved":[88],"look-up":[89],"table":[90],"error,":[94],"while":[95],"trivial":[97],"ignored":[100],"large":[103],"error.":[105],"Experimental":[106],"results":[107],"demonstrate":[108],"outperforms":[112],"state-of-the-art":[114],"accuracy,":[119],"power":[120],"area.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}