{"id":"https://openalex.org/W3083485280","doi":"https://doi.org/10.1109/tcsi.2020.3019460","title":"Weight-Oriented Approximation for Energy-Efficient Neural Network Inference Accelerators","display_name":"Weight-Oriented Approximation for Energy-Efficient Neural Network Inference Accelerators","publication_year":2020,"publication_date":"2020-09-04","ids":{"openalex":"https://openalex.org/W3083485280","doi":"https://doi.org/10.1109/tcsi.2020.3019460","mag":"3083485280"},"language":"en","primary_location":{"id":"doi:10.1109/tcsi.2020.3019460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2020.3019460","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Transactions on Circuits and Systems I: Regular Papers","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/A5088041829","display_name":"Zois-Gerasimos Tasoulas","orcid":"https://orcid.org/0000-0003-2857-7912"},"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":true,"raw_author_name":"Zois-Gerasimos Tasoulas","raw_affiliation_strings":["Southern Illinois University, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"middle","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":false,"raw_author_name":"Georgios Zervakis","raw_affiliation_strings":["Chair for Embedded Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Chair for Embedded Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany","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":["Southern Illinois University, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, Carbondale, IL, USA","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/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":"Hussam Amrouch","raw_affiliation_strings":["Chair for Embedded Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Chair for Embedded Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"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/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, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Chair for Embedded Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5088041829"],"corresponding_institution_ids":["https://openalex.org/I110378019"],"apc_list":null,"apc_paid":null,"fwci":5.4111,"has_fulltext":false,"cited_by_count":91,"citation_normalized_percentile":{"value":0.9651501,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"67","issue":"12","first_page":"4670","last_page":"4683"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/inference","display_name":"Inference","score":0.7848150134086609},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.765932559967041},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6397923231124878},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6118603944778442},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5927621126174927},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5830790996551514},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5548723340034485},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.5254201889038086},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5067949891090393},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45320868492126465},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.444031685590744},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.43072718381881714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38857588171958923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33297792077064514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.279119074344635},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09748420119285583},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08726844191551208},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.077740877866745}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7848150134086609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.765932559967041},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6397923231124878},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6118603944778442},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5927621126174927},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5830790996551514},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5548723340034485},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.5254201889038086},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5067949891090393},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45320868492126465},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.444031685590744},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.43072718381881714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38857588171958923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33297792077064514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.279119074344635},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09748420119285583},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08726844191551208},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.077740877866745},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsi.2020.3019460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2020.3019460","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Transactions on Circuits and Systems I: Regular Papers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1986192284","https://openalex.org/W2005865544","https://openalex.org/W2016491712","https://openalex.org/W2020217519","https://openalex.org/W2067713319","https://openalex.org/W2076536455","https://openalex.org/W2126628495","https://openalex.org/W2194775991","https://openalex.org/W2280900335","https://openalex.org/W2323130283","https://openalex.org/W2346021534","https://openalex.org/W2346205343","https://openalex.org/W2533121491","https://openalex.org/W2559460803","https://openalex.org/W2577531088","https://openalex.org/W2587701905","https://openalex.org/W2606722458","https://openalex.org/W2612139336","https://openalex.org/W2625264446","https://openalex.org/W2742536119","https://openalex.org/W2764043458","https://openalex.org/W2790285612","https://openalex.org/W2793950911","https://openalex.org/W2798993323","https://openalex.org/W2799131456","https://openalex.org/W2808327285","https://openalex.org/W2871705258","https://openalex.org/W2884983013","https://openalex.org/W2890409005","https://openalex.org/W2900509597","https://openalex.org/W2908807758","https://openalex.org/W2911491685","https://openalex.org/W2912921810","https://openalex.org/W2916566087","https://openalex.org/W2921026389","https://openalex.org/W2921967969","https://openalex.org/W2943267175","https://openalex.org/W2944933750","https://openalex.org/W2946374992","https://openalex.org/W2958306322","https://openalex.org/W2963163009","https://openalex.org/W2963674932","https://openalex.org/W2995816250","https://openalex.org/W3011748007","https://openalex.org/W3118608800","https://openalex.org/W4234974086","https://openalex.org/W6637373629","https://openalex.org/W6745148473","https://openalex.org/W6758823024","https://openalex.org/W6760738572","https://openalex.org/W6772029579","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2136583354","https://openalex.org/W2111238207","https://openalex.org/W2760721665","https://openalex.org/W330130819","https://openalex.org/W2288610023","https://openalex.org/W2112044895","https://openalex.org/W3121416282","https://openalex.org/W2281389338","https://openalex.org/W2161705627","https://openalex.org/W179829755"],"abstract_inverted_index":{"Current":[0],"research":[1],"in":[2,11,74,78,195],"the":[3,29,53,91,137,141,145],"area":[4],"of":[5,17,31,70,93,144],"Neural":[6],"Networks":[7],"(NN)":[8],"has":[9],"resulted":[10],"performance":[12],"advancements":[13],"for":[14,100,135,170],"a":[15,75,86,131,192],"variety":[16],"complex":[18],"problems.":[19],"Especially,":[20],"embedded":[21],"system":[22],"applications":[23],"rely":[24],"more":[25,27],"and":[26,41,62,112],"on":[28,189],"utilization":[30],"convolutional":[32],"NNs":[33,180],"to":[34,59,89,140,156],"provide":[35],"services":[36],"such":[37],"as":[38,85],"image/audio":[39],"classification":[40],"object":[42],"detection.":[43],"The":[44,149],"core":[45],"arithmetic":[46],"computation":[47],"performed":[48],"during":[49],"NN":[50,66,113,118,138,172],"inference":[51,114,196],"is":[52,83,154,176],"multiply-accumulate":[54],"(MAC)":[55],"operation.":[56],"In":[57,104],"order":[58],"meet":[60],"tighter":[61,63],"throughput":[64],"constraints,":[65],"accelerators":[67],"integrate":[68],"thousands":[69],"MAC":[71],"units":[72],"resulting":[73],"significant":[76],"increase":[77],"power":[79],"consumption.":[80],"Approximate":[81],"computing":[82,94,110],"established":[84],"design":[87],"alternative":[88],"improve":[90],"efficiency":[92],"systems":[95],"by":[96,116],"trading":[97],"computational":[98],"accuracy":[99,125,142,159,197],"high":[101,185],"energy":[102,165,186],"savings.":[103],"this":[105],"work,":[106],"we":[107],"bring":[108],"approximate":[109,120,146],"principles":[111],"together":[115],"designing":[117],"specific":[119],"multipliers":[121],"that":[122,182],"feature":[123],"multiple":[124],"levels":[126,143],"at":[127],"run-time.":[128],"We":[129],"propose":[130],"time-efficient":[132],"automated":[133],"framework":[134],"mapping":[136,153],"weights":[139],"reconfigurable":[147],"accelerator.":[148],"proposed":[150],"weight-oriented":[151],"approximation":[152],"able":[155],"satisfy":[157],"tight":[158],"loss":[160,194],"thresholds,":[161],"while":[162],"significantly":[163],"reducing":[164],"consumption":[166],"without":[167],"any":[168],"need":[169],"intensive":[171],"retraining.":[173],"Our":[174],"approach":[175],"evaluated":[177],"against":[178],"several":[179],"demonstrating":[181],"it":[183],"delivers":[184],"savings":[187],"(17.8%":[188],"average)":[190],"with":[191],"minimal":[193],"(0.5%).":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":15},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
