{"id":"https://openalex.org/W4414198539","doi":"https://doi.org/10.1109/dac63849.2025.11133083","title":"April: Accuracy-Improved Floating-Point Approximation For Neural Network Accelerators","display_name":"April: Accuracy-Improved Floating-Point Approximation For Neural Network Accelerators","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4414198539","doi":"https://doi.org/10.1109/dac63849.2025.11133083"},"language":"en","primary_location":{"id":"doi:10.1109/dac63849.2025.11133083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102028284","display_name":"Yonghao Chen","orcid":"https://orcid.org/0009-0001-3737-2815"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yonghao Chen","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016822729","display_name":"Jiaxiang Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaxiang Zou","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100443904","display_name":"Xinyu Chen","orcid":"https://orcid.org/0000-0003-2075-7193"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyu Chen","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.857200026512146,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.857200026512146,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.67330002784729},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.6137999892234802},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5710999965667725},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5555999875068665},{"id":"https://openalex.org/keywords/integer","display_name":"Integer (computer science)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.43970000743865967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739799976348877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.67330002784729},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.6137999892234802},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5710999965667725},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.48539999127388},{"id":"https://openalex.org/C97137487","wikidata":"https://www.wikidata.org/wiki/Q729138","display_name":"Integer (computer science)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C150741067","wikidata":"https://www.wikidata.org/wiki/Q2377218","display_name":"Systolic array","level":3,"score":0.42579999566078186},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3357999920845032},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.304500013589859},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac63849.2025.11133083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","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":11,"referenced_works":["https://openalex.org/W2063448486","https://openalex.org/W2111013824","https://openalex.org/W2945030472","https://openalex.org/W2963122961","https://openalex.org/W2963163009","https://openalex.org/W3215062617","https://openalex.org/W3217406393","https://openalex.org/W4287254789","https://openalex.org/W4312443924","https://openalex.org/W4385245566","https://openalex.org/W4386076083"],"related_works":[],"abstract_inverted_index":{"Neural":[0],"Networks":[1],"(NNs)":[2],"have":[3],"achieved":[4],"breakthroughs":[5],"in":[6,91],"computer":[7],"vision":[8],"and":[9,58,64,81,122,150],"natural":[10],"language":[11],"processing.":[12],"However,":[13,43],"modern":[14],"models":[15],"are":[16],"computationally":[17],"expensive,":[18],"with":[19],"floating-point":[20,33],"operations":[21],"posing":[22],"a":[23,72,82,115],"major":[24],"bottleneck.":[25],"Floatingpoint":[26],"approximation,":[27],"such":[28,51],"as":[29,52],"Mitchell\u2019s":[30],"logarithm,":[31],"enables":[32],"multiplication":[34],"using":[35],"simpler":[36],"integer":[37],"additions,":[38],"thereby":[39],"improving":[40],"hardware":[41,56],"efficiency.":[42,66],"its":[44],"practical":[45,110],"adoption":[46],"is":[47,174],"hindered":[48],"by":[49,144],"challenges":[50],"precision":[53,79],"degradation,":[54],"efficient":[55],"integrations,":[57],"management":[59],"of":[60,125],"trade-offs":[61],"between":[62],"accuracy":[63,121],"resource":[65,123],"In":[67],"this":[68,96],"paper,":[69],"we":[70,98,112],"propose":[71],"hardware-efficient":[73],"down-samplingbased":[74],"compensation":[75],"method":[76],"to":[77,86,146,162],"mitigate":[78],"loss":[80],"flexible":[83],"bias":[84],"mechanism":[85],"accommodate":[87],"diverse":[88],"data":[89],"distributions":[90],"NN":[92,105],"models.":[93],"Building":[94],"on":[95],"foundation,":[97],"design":[99,173],"configurable":[100],"systolic":[101,128,136],"arrays":[102,137],"optimized":[103],"for":[104],"accelerators.":[106],"To":[107],"further":[108],"support":[109],"adoption,":[111],"introduce":[113],"April,":[114],"co-design":[116],"framework":[117],"that":[118,134],"balances":[119],"the":[120],"usage":[124],"generated":[126],"synthesizable":[127],"arrays.":[129],"Our":[130,172],"FPGA-based":[131],"evaluations":[132],"demonstrate":[133],"April-generated":[135],"reduce":[138],"root":[139],"mean":[140],"square":[141],"error":[142],"(RMSE)":[143],"up":[145],"$\\mathbf{9":[147],"6":[148],"\\%}$":[149,157],"achieve":[151],"$\\mathbf{3":[152],"4":[153],"\\%":[154],"-5":[155],"2":[156],"area":[158],"reduction":[159],"even":[160],"compared":[161],"INT8-based":[163],"implementations":[164],"while":[165],"maintaining":[166],"comparable":[167],"or":[168],"improved":[169],"model":[170],"accuracy.":[171],"open-sourced":[175],"at":[176],"https://github.com/CLabGit/April":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}