{"id":"https://openalex.org/W4390962825","doi":"https://doi.org/10.48550/arxiv.2401.08225","title":"Efficient and Mathematically Robust Operations for Certified Neural Networks Inference","display_name":"Efficient and Mathematically Robust Operations for Certified Neural Networks Inference","publication_year":2024,"publication_date":"2024-01-16","ids":{"openalex":"https://openalex.org/W4390962825","doi":"https://doi.org/10.48550/arxiv.2401.08225"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2401.08225","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.08225","pdf_url":"https://arxiv.org/pdf/2401.08225","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.08225","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020988053","display_name":"Fabien Geyer","orcid":"https://orcid.org/0000-0002-6522-4385"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geyer, Fabien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056070224","display_name":"Johannes Freitag","orcid":"https://orcid.org/0000-0003-2654-9440"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Freitag, Johannes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051683896","display_name":"Tobias Schulz","orcid":"https://orcid.org/0000-0002-4208-2599"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schulz, Tobias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5104358289","display_name":"Sascha Uhrig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uhrig, Sascha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11697","display_name":"Numerical Methods and Algorithms","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11697","display_name":"Numerical Methods and Algorithms","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11034","display_name":"Digital Filter Design and Implementation","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.6958999633789062},{"id":"https://openalex.org/keywords/certification","display_name":"Certification","score":0.5702967643737793},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5693913698196411},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5613709688186646},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.542064905166626},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4302186369895935},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4233546555042267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42224568128585815},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.37674179673194885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3394952416419983},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15427854657173157},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.12022015452384949}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958999633789062},{"id":"https://openalex.org/C46304622","wikidata":"https://www.wikidata.org/wiki/Q374814","display_name":"Certification","level":2,"score":0.5702967643737793},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5693913698196411},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5613709688186646},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.542064905166626},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4302186369895935},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4233546555042267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42224568128585815},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.37674179673194885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3394952416419983},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15427854657173157},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.12022015452384949},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2401.08225","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.08225","pdf_url":"https://arxiv.org/pdf/2401.08225","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2401.08225","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.08225","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.08225","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.08225","pdf_url":"https://arxiv.org/pdf/2401.08225","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390962825.pdf","grobid_xml":"https://content.openalex.org/works/W4390962825.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2066052364","https://openalex.org/W2023743128","https://openalex.org/W4243365217","https://openalex.org/W2224296908","https://openalex.org/W3109981693","https://openalex.org/W2381980429","https://openalex.org/W2384206113","https://openalex.org/W645983410","https://openalex.org/W2401692867","https://openalex.org/W2057053421"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"machine":[3],"learning":[4],"(ML)":[5],"and":[6,14,53,63,76,85],"neural":[7],"networks":[8],"(NNs)":[9],"have":[10,40],"gained":[11],"widespread":[12],"use":[13],"attention":[15],"across":[16],"various":[17],"domains,":[18],"particularly":[19],"in":[20],"transportation":[21],"for":[22,31],"achieving":[23],"autonomy,":[24],"including":[25],"the":[26,44,50,60,64,68,119,131],"emergence":[27],"of":[28,46,100,128,134],"flying":[29],"taxis":[30],"urban":[32],"air":[33],"mobility":[34],"(UAM).":[35],"However,":[36],"concerns":[37],"about":[38],"certification":[39],"come":[41],"up,":[42],"compelling":[43],"development":[45],"standardized":[47],"processes":[48],"encompassing":[49],"entire":[51],"ML":[52],"NN":[54],"pipeline.":[55],"This":[56],"paper":[57],"delves":[58],"into":[59],"inference":[61],"stage":[62],"requisite":[65],"hardware,":[66],"highlighting":[67],"challenges":[69],"associated":[70],"with":[71],"IEEE":[72],"754":[73],"floating-point":[74,107],"arithmetic":[75,102],"proposing":[77],"alternative":[78],"number":[79],"representations.":[80],"By":[81],"evaluating":[82],"diverse":[83],"summation":[84],"dot":[86],"product":[87],"algorithms,":[88],"we":[89,117],"aim":[90],"to":[91,95,123],"mitigate":[92],"issues":[93],"related":[94],"non-associativity.":[96],"Additionally,":[97],"our":[98],"exploration":[99],"fixed-point":[101],"reveals":[103],"its":[104],"advantages":[105],"over":[106],"methods,":[108],"demonstrating":[109],"significant":[110],"hardware":[111],"efficiencies.":[112],"Employing":[113],"an":[114,125],"empirical":[115],"approach,":[116],"ascertain":[118],"optimal":[120],"bit-width":[121,135],"necessary":[122],"attain":[124],"acceptable":[126],"level":[127],"accuracy,":[129],"considering":[130],"inherent":[132],"complexity":[133],"optimization.":[136]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2024-01-18T00:00:00"}