{"id":"https://openalex.org/W3132870973","doi":"https://doi.org/10.1109/dac18074.2021.9586092","title":"Control Variate Approximation for DNN Accelerators","display_name":"Control Variate Approximation for DNN Accelerators","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3132870973","doi":"https://doi.org/10.1109/dac18074.2021.9586092","mag":"3132870973"},"language":"en","primary_location":{"id":"doi:10.1109/dac18074.2021.9586092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2102.09642","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051495057","display_name":"Georgios Zervakis","orcid":"https://orcid.org/0000-0001-8110-7122"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Georgios Zervakis","raw_affiliation_strings":["Chair for Embedded Systems (CES), Karlsruhe Institute of Technology, Karlsruhe, Germany","Karlsruhe Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Chair for Embedded Systems (CES), Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]},{"raw_affiliation_string":"Karlsruhe Institute of Technology","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069848169","display_name":"Ourania Spantidi","orcid":"https://orcid.org/0000-0002-3631-1607"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]},{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Ourania Spantidi","raw_affiliation_strings":["Department of Electrical, Computer and Biomedical Engineering, Southern Illinois University, Carbondale, U.S.A","Karlsruhe Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, Southern Illinois University, Carbondale, U.S.A","institution_ids":["https://openalex.org/I110378019"]},{"raw_affiliation_string":"Karlsruhe Institute of Technology","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052217926","display_name":"Iraklis Anagnostopoulos","orcid":"https://orcid.org/0000-0003-0985-3045"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Iraklis Anagnostopoulos","raw_affiliation_strings":["Department of Electrical, Computer and Biomedical Engineering, Southern Illinois University, Carbondale, U.S.A","Southern Illinois University Carbondale#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, Southern Illinois University, Carbondale, U.S.A","institution_ids":["https://openalex.org/I110378019"]},{"raw_affiliation_string":"Southern Illinois University Carbondale#TAB#","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059133190","display_name":"Hussam Amrouch","orcid":"https://orcid.org/0000-0002-5649-3102"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]},{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Hussam Amrouch","raw_affiliation_strings":["Chair of Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany","Southern Illinois University Carbondale#TAB#"],"affiliations":[{"raw_affiliation_string":"Chair of Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]},{"raw_affiliation_string":"Southern Illinois University Carbondale#TAB#","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063508488","display_name":"J\u00f6rg Henkel","orcid":"https://orcid.org/0000-0001-9602-2922"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]},{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jorg Henkel","raw_affiliation_strings":["Chair for Embedded Systems (CES), Karlsruhe Institute of Technology, Karlsruhe, Germany","Univ. of Stuttgart#TAB#"],"affiliations":[{"raw_affiliation_string":"Chair for Embedded Systems (CES), Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]},{"raw_affiliation_string":"Univ. of Stuttgart#TAB#","institution_ids":["https://openalex.org/I100066346"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5051495057"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":0.0969,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34946672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.996399998664856,"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":0.9944999814033508,"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/control-variates","display_name":"Control variates","score":0.8722437620162964},{"id":"https://openalex.org/keywords/random-variate","display_name":"Random variate","score":0.8281064033508301},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.7221540212631226},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6300082206726074},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6134534478187561},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5155084133148193},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5096597671508789},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.46188443899154663},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.45946359634399414},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4446488320827484},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.42847511172294617},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41867533326148987},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4100933074951172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3667086362838745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2665315568447113},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.14822447299957275},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14218711853027344},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.07445338368415833}],"concepts":[{"id":"https://openalex.org/C121683094","wikidata":"https://www.wikidata.org/wiki/Q3554721","display_name":"Control variates","level":5,"score":0.8722437620162964},{"id":"https://openalex.org/C141547133","wikidata":"https://www.wikidata.org/wiki/Q7291996","display_name":"Random variate","level":3,"score":0.8281064033508301},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.7221540212631226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6300082206726074},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6134534478187561},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5155084133148193},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5096597671508789},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.46188443899154663},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.45946359634399414},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4446488320827484},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.42847511172294617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41867533326148987},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4100933074951172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3667086362838745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2665315568447113},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.14822447299957275},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14218711853027344},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.07445338368415833},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/dac18074.2021.9586092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.09642","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.09642","pdf_url":"https://arxiv.org/pdf/2102.09642","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"mag:3132870973","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2102.09642","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2102.09642","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2102.09642","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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:2102.09642","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.09642","pdf_url":"https://arxiv.org/pdf/2102.09642","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"display_name":"Affordable and clean energy","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1982780415","https://openalex.org/W1986192284","https://openalex.org/W2323130283","https://openalex.org/W2533121491","https://openalex.org/W2606722458","https://openalex.org/W2612139336","https://openalex.org/W2769672439","https://openalex.org/W2871705258","https://openalex.org/W2897107698","https://openalex.org/W2911491685","https://openalex.org/W2944933750","https://openalex.org/W2953212265","https://openalex.org/W2958306322","https://openalex.org/W3007372856","https://openalex.org/W3011748007","https://openalex.org/W3036417437","https://openalex.org/W3036553373","https://openalex.org/W3083485280","https://openalex.org/W3091745343","https://openalex.org/W4235991875","https://openalex.org/W6758823024"],"related_works":["https://openalex.org/W3213930560","https://openalex.org/W3016255156","https://openalex.org/W3176381659","https://openalex.org/W3146135289","https://openalex.org/W3035718539","https://openalex.org/W3163232994","https://openalex.org/W3007132260","https://openalex.org/W1877071675","https://openalex.org/W2257410811","https://openalex.org/W2921234600","https://openalex.org/W2144234629","https://openalex.org/W2972519944","https://openalex.org/W3204822007","https://openalex.org/W2997839189","https://openalex.org/W2962770523","https://openalex.org/W3126837587","https://openalex.org/W36919089","https://openalex.org/W2129718951","https://openalex.org/W846742640","https://openalex.org/W3206784712"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,59],"introduce":[4],"a":[5,99],"control":[6,20,56,88],"variate":[7,21,57,89],"approximation":[8,90],"technique":[9,22],"for":[10,75,98],"low":[11],"error":[12,39],"approximate":[13,42],"Deep":[14],"Neural":[15],"Network":[16],"(DNN)":[17],"accelerators.":[18,68],"The":[19],"is":[23],"used":[24],"in":[25,44],"Monte":[26],"Carlo":[27],"methods":[28],"to":[29,41,52,64,83],"achieve":[30],"variance":[31],"reduction.":[32],"Our":[33,69],"approach":[34],"significantly":[35],"decreases":[36],"the":[37,84],"induced":[38],"due":[40],"multiplications":[43],"DNN":[45,67],"inference,":[46],"without":[47],"requiring":[48],"time-exhaustive":[49],"retraining":[50],"compared":[51,82],"state-of-the-art.":[53],"Leveraging":[54],"our":[55,87],"method,":[58],"use":[60],"highly":[61],"approximated":[62],"multipliers":[63],"generate":[65],"power-optimized":[66],"experimental":[70],"evaluation":[71],"on":[72],"six":[73],"DNNs,":[74],"Cifar-10":[76],"and":[77,94],"Cifar100":[78],"datasets,":[79],"demonstrates":[80],"that,":[81],"accurate":[85],"design,":[86],"achieves":[91],"same":[92],"performance":[93],"24%":[95],"power":[96],"reduction":[97],"merely":[100],"0.16%":[101],"accuracy":[102],"loss.":[103]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}