{"id":"https://openalex.org/W2924168890","doi":"https://doi.org/10.1145/3299874.3319493","title":"On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning","display_name":"On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2924168890","doi":"https://doi.org/10.1145/3299874.3319493","mag":"2924168890"},"language":"en","primary_location":{"id":"doi:10.1145/3299874.3319493","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299874.3319493","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299874.3319493","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3299874.3319493","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079352628","display_name":"Bharat Prakash","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bharat Prakash","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011426186","display_name":"Mark Horton","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Horton","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079459509","display_name":"Nicholas R. Waytowich","orcid":"https://orcid.org/0000-0002-3786-0675"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas R. Waytowich","raw_affiliation_strings":["US Army Research Laboratory, Aberdeen, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"US Army Research Laboratory, Aberdeen, MD, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110927360","display_name":"William Hairston","orcid":null},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William David Hairston","raw_affiliation_strings":["US Army Research Laboratory, Aberdeen, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"US Army Research Laboratory, Aberdeen, MD, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114778025","display_name":"Tim Oates","orcid":"https://orcid.org/0000-0002-8655-747X"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tim Oates","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084010501","display_name":"Tinoosh Mohsenin","orcid":"https://orcid.org/0000-0001-5551-2124"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tinoosh Mohsenin","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9152,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.78707859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"507","last_page":"512"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9990000128746033,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.991599977016449,"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/autoencoder","display_name":"Autoencoder","score":0.9097898602485657},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8650341629981995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8312308192253113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6839855909347534},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.656484067440033},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5822054743766785},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5026793479919434},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49025657773017883},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.45678481459617615},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4527859091758728},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.42628708481788635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41062217950820923},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07924053072929382}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9097898602485657},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8650341629981995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8312308192253113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6839855909347534},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.656484067440033},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5822054743766785},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5026793479919434},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49025657773017883},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.45678481459617615},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4527859091758728},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.42628708481788635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41062217950820923},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07924053072929382},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3299874.3319493","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299874.3319493","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299874.3319493","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3299874.3319493","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299874.3319493","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299874.3319493","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2155027007","https://openalex.org/W2736601468","https://openalex.org/W2781726626","https://openalex.org/W2884780389","https://openalex.org/W2889279355","https://openalex.org/W2963367680"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W2161705627"],"abstract_inverted_index":{"In":[0],"autonomous":[1,158],"embedded":[2,74,86,151],"systems,":[3],"it":[4],"is":[5,64],"often":[6],"vital":[7,65],"to":[8,22,48,66,119,165,171],"reduce":[9],"the":[10,16,82,90,98,113,117,121,124,130,133,144],"amount":[11],"of":[12,97,123,132,143,146,186],"actions":[13],"taken":[14],"in":[15,156],"real":[17],"world":[18],"and":[19,39,88,94,106,128,153],"energy":[20,95],"required":[21],"learn":[23,68],"a":[24,172,177],"policy.":[25],"Training":[26],"reinforcement":[27,134],"learning":[28,72,135],"agents":[29],"from":[30],"high":[31,50],"dimensional":[32,51],"image":[33],"representations":[34],"can":[35],"be":[36],"very":[37],"expensive":[38],"time":[40],"consuming.":[41],"Autoencoders":[42],"are":[43],"deep":[44],"neural":[45],"network":[46],"used":[47],"compress":[49],"data":[52],"such":[53],"as":[54],"pixelated":[55],"images":[56],"into":[57],"small":[58],"latent":[59],"representations.":[60],"This":[61],"compression":[62],"model":[63,80,107],"efficiently":[67],"policies,":[69],"especially":[70],"when":[71],"on":[73,81,150],"systems.":[75],"We":[76],"have":[77,111,139],"implemented":[78],"this":[79],"NVIDIA":[83],"Jetson":[84],"TX2":[85],"GPU,":[87],"evaluated":[89],"power":[91],"consumption,":[92],"throughput,":[93],"consumption":[96],"autoencoders":[99],"for":[100],"various":[101],"CPU/GPU":[102],"core":[103],"combinations,":[104],"frequencies,":[105],"parameters.":[108],"Additionally,":[109],"we":[110,138,162],"shown":[112],"reconstructions":[114],"generated":[115,125],"by":[116],"autoencoder":[118],"analyze":[120],"quality":[122],"compressed":[126],"representation":[127],"also":[129],"performance":[131,169],"agent.":[136],"Finally,":[137],"presented":[140],"an":[141],"assessment":[142],"viability":[145],"training":[147],"these":[148],"models":[149],"systems":[152],"their":[154],"usefulness":[155],"developing":[157],"policies.":[159],"Using":[160],"autoencoders,":[161],"were":[163],"able":[164],"achieve":[166],"4-5X":[167],"improved":[168],"compared":[170],"baseline":[173],"RL":[174],"agent":[175],"with":[176],"convolutional":[178],"feature":[179],"extractor,":[180],"while":[181],"using":[182],"less":[183],"than":[184],"2W":[185],"power.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
