{"id":"https://openalex.org/W4403391192","doi":"https://doi.org/10.1109/fmec62297.2024.10710277","title":"FPGA-based Acceleration of Deep Q-Networks with STANN-RL","display_name":"FPGA-based Acceleration of Deep Q-Networks with STANN-RL","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4403391192","doi":"https://doi.org/10.1109/fmec62297.2024.10710277"},"language":"en","primary_location":{"id":"doi:10.1109/fmec62297.2024.10710277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fmec62297.2024.10710277","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Fog and Mobile Edge Computing (FMEC)","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/A5084321890","display_name":"Marc Rothmann","orcid":"https://orcid.org/0000-0003-2886-8197"},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Marc Rothmann","raw_affiliation_strings":["Osnabr&#x00FC;ck University,Computer Science Department,Osnabr&#x00FC;ck,Germany"],"affiliations":[{"raw_affiliation_string":"Osnabr&#x00FC;ck University,Computer Science Department,Osnabr&#x00FC;ck,Germany","institution_ids":["https://openalex.org/I170658231"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028498307","display_name":"Mario Porrmann","orcid":"https://orcid.org/0000-0003-1005-5753"},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mario Porrmann","raw_affiliation_strings":["Osnabr&#x00FC;ck University,Computer Science Department,Osnabr&#x00FC;ck,Germany"],"affiliations":[{"raw_affiliation_string":"Osnabr&#x00FC;ck University,Computer Science Department,Osnabr&#x00FC;ck,Germany","institution_ids":["https://openalex.org/I170658231"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084321890"],"corresponding_institution_ids":["https://openalex.org/I170658231"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67797575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"99","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.6146000027656555,"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/T12676","display_name":"Machine Learning and ELM","score":0.6146000027656555,"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/T13382","display_name":"Robotics and Automated Systems","score":0.5609999895095825,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.5397999882698059,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/acceleration","display_name":"Acceleration","score":0.852868378162384},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.8495678901672363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6980714201927185},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.4639451503753662},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.35262709856033325},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09380009770393372}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.852868378162384},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.8495678901672363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6980714201927185},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4639451503753662},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.35262709856033325},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09380009770393372},{"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:10.1109/fmec62297.2024.10710277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fmec62297.2024.10710277","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Fog and Mobile Edge Computing (FMEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.6000000238418579}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320882","display_name":"Volkswagen Foundation","ror":"https://ror.org/03bsmfz84"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2145339207","https://openalex.org/W2572023225","https://openalex.org/W2763502612","https://openalex.org/W2784155501","https://openalex.org/W2787938642","https://openalex.org/W2793035934","https://openalex.org/W2889068523","https://openalex.org/W2931767035","https://openalex.org/W2950395671","https://openalex.org/W2962684312","https://openalex.org/W2963864421","https://openalex.org/W2971573826","https://openalex.org/W2996037775","https://openalex.org/W3035681682","https://openalex.org/W3128973049","https://openalex.org/W3169732227","https://openalex.org/W4210397953","https://openalex.org/W4229017035","https://openalex.org/W4255900881","https://openalex.org/W4295312788","https://openalex.org/W4321637127","https://openalex.org/W4321637160","https://openalex.org/W4385696544","https://openalex.org/W4387195391","https://openalex.org/W4390188283","https://openalex.org/W4401537809","https://openalex.org/W6631190155","https://openalex.org/W6637967152","https://openalex.org/W6684921986","https://openalex.org/W6735579001","https://openalex.org/W6747823896","https://openalex.org/W6748839928","https://openalex.org/W6749032995","https://openalex.org/W6766978945","https://openalex.org/W6772005887","https://openalex.org/W6922480057"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2111241003","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W2096844293","https://openalex.org/W2363944576","https://openalex.org/W2351041855","https://openalex.org/W2570254841"],"abstract_inverted_index":{"Deep":[0,55,86,135],"Reinforcement":[1,136],"Learning":[2,137],"is":[3,60,89],"a":[4,31,49,63,69,74,94,100],"promising":[5],"research":[6],"domain":[7],"with":[8,73,122],"many":[9],"interesting":[10],"applications":[11],"in":[12,22,139],"various":[13],"fields,":[14],"from":[15,43],"protein":[16],"folding":[17],"to":[18,37,99],"real-time":[19],"decision":[20],"making":[21],"Internet":[23],"of":[24,34,112,131],"Things":[25],"applications.":[26],"The":[27,58,85,103],"state-of-the-art":[28],"algorithms":[29,138],"require":[30],"large":[32],"amount":[33],"computing":[35],"resources":[36],"train":[38],"and":[39,66,92,117],"could":[40],"benefit":[41],"significantly":[42],"hardware":[44,51],"acceleration.":[45],"This":[46,127],"paper":[47],"presents":[48],"new":[50],"accelerator":[52,88],"for":[53,83,109,134],"the":[54,106,110,113,129,140],"Q-Network":[56,87],"algorithm.":[57],"algorithm":[59],"implemented":[61],"on":[62],"Xilinx":[64],"Versal":[65],"integrated":[67],"into":[68],"heterogeneous":[70],"training":[71,116],"system":[72],"host":[75],"PC":[76],"that":[77],"runs":[78],"common":[79,123],"reinforcement":[80,124],"learning":[81,125],"environments":[82],"benchmarking.":[84],"highly":[90],"configurable":[91],"achieves":[93],"$2.5":[95],"\\times$":[96],"speed-up":[97],"compared":[98],"CPU":[101],"baseline.":[102],"implementation":[104,130],"uses":[105],"STANN":[107,121],"library":[108],"design":[111],"neural":[114],"network":[115],"introduces":[118],"STANN-RL,":[119],"extending":[120],"functionality.":[126],"facilitates":[128],"additional":[132],"accelerators":[133],"future.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
