{"id":"https://openalex.org/W4280636456","doi":"https://doi.org/10.23919/date54114.2022.9774739","title":"Hardware Acceleration of Explainable Machine Learning","display_name":"Hardware Acceleration of Explainable Machine Learning","publication_year":2022,"publication_date":"2022-03-14","ids":{"openalex":"https://openalex.org/W4280636456","doi":"https://doi.org/10.23919/date54114.2022.9774739"},"language":"en","primary_location":{"id":"doi:10.23919/date54114.2022.9774739","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date54114.2022.9774739","pdf_url":null,"source":{"id":"https://openalex.org/S4363607924","display_name":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","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/A5013876571","display_name":"Zhixin Pan","orcid":"https://orcid.org/0000-0001-9136-1730"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhixin Pan","raw_affiliation_strings":["University of Florida,Department of Computer & Information Science & Engineering,Gainesville,Florida,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,Department of Computer & Information Science & Engineering,Gainesville,Florida,USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006818844","display_name":"Prabhat Mishra","orcid":"https://orcid.org/0000-0003-3653-6221"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prabhat Mishra","raw_affiliation_strings":["University of Florida,Department of Computer & Information Science & Engineering,Gainesville,Florida,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,Department of Computer & Information Science & Engineering,Gainesville,Florida,USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1127","last_page":"1130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9896000027656555,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9843000173568726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8139380216598511},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.8056045770645142},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.7225459218025208},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6615900993347168},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5748775005340576},{"id":"https://openalex.org/keywords/graphics-processing-unit","display_name":"Graphics processing unit","score":0.544227123260498},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.5397106409072876},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5247393250465393},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44405171275138855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.374324232339859},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36914628744125366},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3296574354171753},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3104385435581207},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.263314813375473},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24330464005470276},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16067808866500854}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8139380216598511},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.8056045770645142},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.7225459218025208},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6615900993347168},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5748775005340576},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.544227123260498},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.5397106409072876},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5247393250465393},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44405171275138855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.374324232339859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36914628744125366},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3296574354171753},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3104385435581207},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.263314813375473},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24330464005470276},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16067808866500854},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date54114.2022.9774739","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date54114.2022.9774739","pdf_url":null,"source":{"id":"https://openalex.org/S4363607924","display_name":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2171294354","display_name":null,"funder_award_id":"CCF-1908131","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2606722458","https://openalex.org/W2970971581","https://openalex.org/W2978121167","https://openalex.org/W3018558340","https://openalex.org/W3111154820","https://openalex.org/W3115527251","https://openalex.org/W3116877279","https://openalex.org/W3122511173","https://openalex.org/W3128775242","https://openalex.org/W3131920484","https://openalex.org/W3176818990","https://openalex.org/W3185872651","https://openalex.org/W4213366026","https://openalex.org/W4295312788","https://openalex.org/W6687483927","https://openalex.org/W6737842964","https://openalex.org/W6773126855","https://openalex.org/W6787576791","https://openalex.org/W6798937889","https://openalex.org/W6843845902"],"related_works":["https://openalex.org/W2142903600","https://openalex.org/W4280636456","https://openalex.org/W4319952061","https://openalex.org/W3103034165","https://openalex.org/W4310584535","https://openalex.org/W3154092384","https://openalex.org/W4295935044","https://openalex.org/W4307927141","https://openalex.org/W3159906349","https://openalex.org/W2625222559"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"(ML)":[2],"is":[3,130],"successful":[4],"in":[5,9,42,109,134,164,192],"achieving":[6],"human-level":[7],"performance":[8],"various":[10],"fields.":[11],"However,":[12],"it":[13,156],"lacks":[14],"the":[15,36,94,122,131,146],"ability":[16,163],"to":[17,22,55,82,174,207],"explain":[18],"an":[19,50,79],"outcome":[20,176],"due":[21],"its":[23],"black-box":[24],"nature.":[25],"While":[26],"recent":[27],"efforts":[28],"on":[29,186,196,204],"explainable":[30,86,111,139],"ML":[31,87],"has":[32],"received":[33],"significant":[34],"attention,":[35],"existing":[37,66,208],"solutions":[38],"are":[39,68],"not":[40,69],"applicable":[41],"real-time":[43,175],"systems":[44],"since":[45],"they":[46],"map":[47],"interpretability":[48],"as":[49,198,200],"optimization":[51],"problem,":[52],"which":[53],"leads":[54],"numerous":[56],"iterations":[57],"of":[58,85,96,124,138,160],"time-consuming":[59],"complex":[60],"computations.":[61,167],"To":[62,121],"make":[63],"matters":[64],"worse,":[65],"implementations":[67],"amenable":[70],"for":[71],"hardware-based":[72],"acceleration.":[73],"In":[74],"this":[75,114],"paper,":[76],"we":[77],"propose":[78],"efficient":[80],"framework":[81],"enable":[83],"acceleration":[84,137,209],"procedure":[88],"with":[89],"hardware":[90,136],"accelerators.":[91],"We":[92],"explore":[93],"effectiveness":[95],"both":[97],"Tensor":[98],"Processing":[99,104],"Unit":[100,105],"(TPU)":[101],"and":[102,151,154],"Graphics":[103],"(GPU)":[106],"based":[107],"architectures":[108],"accelerating":[110,165],"ML.":[112,140],"Specifically,":[113],"paper":[115],"makes":[116],"three":[117],"important":[118],"contributions.":[119],"(1)":[120],"best":[123],"our":[125,127],"knowledge,":[126],"proposed":[128,143,170,183],"work":[129],"first":[132],"attempt":[133],"enabling":[135],"(2)":[141],"Our":[142,169],"solution":[144],"exploits":[145],"synergy":[147],"between":[148],"matrix":[149,166],"convolution":[150],"Fourier":[152],"transform,":[153],"therefore,":[155],"takes":[157],"full":[158],"advantage":[159],"TPU's":[161],"inherent":[162],"(3)":[168],"approach":[171,184],"can":[172,188],"lead":[173],"interpretation.":[177],"Extensive":[178],"experimental":[179],"evaluation":[180],"demonstrates":[181],"that":[182],"deployed":[185],"TPU":[187],"provide":[189],"drastic":[190],"improvement":[191],"interpretation":[193],"time":[194],"(39x":[195],"average)":[197,205],"well":[199],"energy":[201],"efficiency":[202],"(69x":[203],"compared":[206],"techniques.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}