{"id":"https://openalex.org/W4283810319","doi":"https://doi.org/10.1109/ncc55593.2022.9806738","title":"Linear Classification on Noisy Hardware","display_name":"Linear Classification on Noisy Hardware","publication_year":2022,"publication_date":"2022-05-24","ids":{"openalex":"https://openalex.org/W4283810319","doi":"https://doi.org/10.1109/ncc55593.2022.9806738"},"language":"en","primary_location":{"id":"doi:10.1109/ncc55593.2022.9806738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc55593.2022.9806738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 National Conference on Communications (NCC)","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/A5003664419","display_name":"Prakruthi Pradeep","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Prakruthi Pradeep","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University,Pittsburgh,USA","School of Computer Science, Carnegie Mellon University, Pittsburgh, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University,Pittsburgh,USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044386835","display_name":"Venkata Sai Chelagamsetty","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"facility","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkata Sai Chelagamsetty","raw_affiliation_strings":["Indian Institute of Technology Madras,Department of Electrical Engineering,Chennai,India","Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Madras,Department of Electrical Engineering,Chennai,India","institution_ids":["https://openalex.org/I24676775"]},{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061667884","display_name":"Avhishek Chatterjee","orcid":"https://orcid.org/0000-0003-2642-7582"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"facility","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Avhishek Chatterjee","raw_affiliation_strings":["Indian Institute of Technology Madras,Department of Electrical Engineering,Chennai,India","Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Madras,Department of Electrical Engineering,Chennai,India","institution_ids":["https://openalex.org/I24676775"]},{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003664419"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0596439,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"356","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9977999925613403,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9977999925613403,"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/T10320","display_name":"Neural Networks and Applications","score":0.995199978351593,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.7193765044212341},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6108151078224182},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5455155372619629},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5420618057250977},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5417152047157288},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.534282922744751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5123707056045532},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.49784350395202637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48077744245529175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3628184199333191},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35211390256881714},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3066927194595337},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13994091749191284},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08020555973052979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7193765044212341},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6108151078224182},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5455155372619629},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5420618057250977},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5417152047157288},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.534282922744751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5123707056045532},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.49784350395202637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48077744245529175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3628184199333191},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35211390256881714},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3066927194595337},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13994091749191284},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08020555973052979},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc55593.2022.9806738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc55593.2022.9806738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 National Conference on Communications (NCC)","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":18,"referenced_works":["https://openalex.org/W2066789378","https://openalex.org/W2399958287","https://openalex.org/W2527151150","https://openalex.org/W2732951378","https://openalex.org/W2741318576","https://openalex.org/W2890613163","https://openalex.org/W2895367957","https://openalex.org/W2960833983","https://openalex.org/W2970859626","https://openalex.org/W2975461158","https://openalex.org/W2986336370","https://openalex.org/W3080303273","https://openalex.org/W3198011113","https://openalex.org/W4236362309","https://openalex.org/W4250589301","https://openalex.org/W6740159687","https://openalex.org/W6742116046","https://openalex.org/W6767376156"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2055243143","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2360673138","https://openalex.org/W2809370583","https://openalex.org/W2333722679","https://openalex.org/W4255628145"],"abstract_inverted_index":{"Motivated":[0],"by":[1,27,67],"the":[2,14,23,55,92],"growing":[3],"interest":[4],"in":[5],"machine":[6],"learning":[7],"on":[8,22,32,54,80,101],"nanoscale":[9],"edge":[10],"devices,":[11],"we":[12,61],"study":[13],"effect":[15],"of":[16,25,77],"hardware":[17,43],"noise":[18,44],"and":[19,34,45,75,82],"quantization":[20],"errors":[21,46],"accuracy":[24],"inference":[26],"linear":[28,71],"classifiers.":[29],"Our":[30],"experiments":[31],"synthetic":[33,81],"real":[35,83],"data":[36,84],"sets":[37,85],"using":[38],"well":[39],"accepted":[40],"models":[41],"for":[42,105],"show":[47,86],"that":[48,87],"they":[49],"have":[50],"a":[51,98,102],"significant":[52],"impact":[53],"accuracy.":[56],"For":[57],"mitigating":[58],"those":[59],"effects,":[60],"propose":[62],"an":[63],"easily":[64],"implementable":[65],"strategy":[66,90],"combining":[68],"insights":[69],"from":[70],"classification,":[72],"convex":[73],"analysis":[74],"concentration":[76],"measure.":[78],"Evaluations":[79],"our":[88],"simple":[89],"improves":[91],"performance":[93],"significantly.":[94],"We":[95],"end":[96],"with":[97],"brief":[99],"discussion":[100],"few":[103],"avenues":[104],"further":[106],"explorations.":[107]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}