{"id":"https://openalex.org/W3213742958","doi":"https://doi.org/10.1109/dac18074.2021.9586213","title":"FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism","display_name":"FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3213742958","doi":"https://doi.org/10.1109/dac18074.2021.9586213","mag":"3213742958"},"language":"en","primary_location":{"id":"doi:10.1109/dac18074.2021.9586213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586213","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":"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/A5060522931","display_name":"Je Yang","orcid":"https://orcid.org/0009-0003-2024-6542"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Je Yang","raw_affiliation_strings":["School of Electrical Engineering, KAIST"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, KAIST","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075135160","display_name":"Seongmin Hong","orcid":"https://orcid.org/0000-0001-6904-8094"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongmin Hong","raw_affiliation_strings":["School of Electrical Engineering, KAIST"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, KAIST","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100447377","display_name":"Joo-Young Kim","orcid":"https://orcid.org/0000-0003-1099-1496"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joo-Young Kim","raw_affiliation_strings":["School of Electrical Engineering, KAIST"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, KAIST","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060522931"],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":2.8553,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.92339551,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"259","last_page":"264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9983000159263611,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9983000159263611,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.982200026512146,"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":0.9628000259399414,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8208948373794556},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7444924116134644},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6508299112319946},{"id":"https://openalex.org/keywords/parallelism","display_name":"Parallelism (grammar)","score":0.6098995208740234},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.46772587299346924},{"id":"https://openalex.org/keywords/fixed-point","display_name":"Fixed point","score":0.45046964287757874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34797340631484985},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13204100728034973},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06973996758460999}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8208948373794556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7444924116134644},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6508299112319946},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.6098995208740234},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.46772587299346924},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.45046964287757874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34797340631484985},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13204100728034973},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06973996758460999},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac18074.2021.9586213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586213","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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W2063054322","https://openalex.org/W2119144962","https://openalex.org/W2121615981","https://openalex.org/W2158782408","https://openalex.org/W2173248099","https://openalex.org/W2362143032","https://openalex.org/W2469490737","https://openalex.org/W2572023225","https://openalex.org/W2736601468","https://openalex.org/W2787938642","https://openalex.org/W2798705390","https://openalex.org/W2809371234","https://openalex.org/W2931767035","https://openalex.org/W2963122961","https://openalex.org/W2963864421","https://openalex.org/W2963871073","https://openalex.org/W2964043796","https://openalex.org/W2964299589","https://openalex.org/W2977925801","https://openalex.org/W2999803881","https://openalex.org/W3035681682","https://openalex.org/W3035718760","https://openalex.org/W4238341497","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6677580257","https://openalex.org/W6678317597","https://openalex.org/W6684921986","https://openalex.org/W6692846177","https://openalex.org/W6720242923","https://openalex.org/W6741002519","https://openalex.org/W6768463214"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2045183646","https://openalex.org/W2162409446","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W1595672120","https://openalex.org/W4230999561"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1,74],"learning":[2,75],"(DRL)":[3],"is":[4,206],"a":[5,36,72,93,99,115,127,187],"powerful":[6],"technology":[7],"to":[8,26,34,107,136,149],"deal":[9],"with":[10],"decision-making":[11],"problem":[12],"in":[13,31,103,181],"various":[14],"application":[15],"domains":[16],"such":[17],"as":[18],"robotics":[19],"and":[20,85,134,140,169,222,232,250],"gaming,":[21],"by":[22,112,177],"allowing":[23],"an":[24,32],"agent":[25],"learn":[27],"its":[28,216],"action":[29,183],"policy":[30],"environment":[33,168],"maximize":[35],"cumulative":[37],"reward.":[38],"Unlike":[39],"supervised":[40],"models":[41],"which":[42,80,105,205,227],"actively":[43],"use":[44],"data":[45,83,110],"quantization,":[46],"DRL":[47,167,189],"still":[48],"uses":[49],"the":[50,89,109,152,162,166,170,173,194,211,246,265],"single-precision":[51],"floating-point":[52],"for":[53,88],"training":[54,101,119,141,203],"accuracy":[55],"while":[56],"it":[57,261],"suffers":[58],"from":[59],"computationally":[60],"intensive":[61],"deep":[62,73],"neural":[63],"network":[64],"(DNN)":[65],"computations.":[66],"In":[67,214],"this":[68],"paper,":[69],"we":[70],"present":[71],"acceleration":[76,256],"platform":[77,196],"named":[78],"FIXAR,":[79],"employs":[81,131],"fixed-point":[82],"types":[84],"arithmetic":[86],"units":[87],"first":[90],"time":[91,120],"using":[92,258],"SW/HW":[94],"co-design":[95],"approach.":[96],"We":[97,124,156],"propose":[98],"quantization-aware":[100],"algorithm":[102,190],"fixed-point,":[104],"enables":[106],"reduce":[108],"precision":[111],"half":[113],"after":[114],"certain":[116],"amount":[117],"of":[118,240,264],"without":[121],"losing":[122],"accuracy.":[123],"also":[125,244],"design":[126],"FPGA":[128,171,217],"accelerator":[129,218],"that":[130],"adaptive":[132],"dataflow":[133],"parallelism":[135],"handle":[137],"both":[138],"inference":[139],"operations.":[142],"Its":[143],"processing":[144],"element":[145],"has":[146],"configurable":[147],"datapath":[148],"efficiently":[150],"support":[151],"proposed":[153],"quantized-aware":[154],"training.":[155],"validate":[157],"our":[158],"FIXAR":[159,195,243],"platform,":[160],"where":[161],"host":[163],"CPU":[164],"emulates":[165],"accelerates":[172],"agent\u2019s":[174],"DNN":[175,268],"operations,":[176],"running":[178],"multiple":[179],"benchmarks":[180],"continuous":[182],"spaces":[184],"based":[185],"on":[186],"latest":[188],"called":[191],"DDPG.":[192],"Finally,":[193],"achieves":[197],"25293.3":[198],"inferences":[199],"per":[200],"second":[201],"(IPS)":[202],"throughput,":[204],"2.7":[207],"times":[208,230,234],"higher":[209,231],"than":[210,238],"CPU-GPU":[212],"platform.":[213],"addition,":[215],"shows":[219,245],"53826.8":[220],"IPS":[221,248],"2638.0":[223],"IPS/W":[224],"energy":[225,236,251],"efficiency,":[226],"are":[228],"5.5":[229],"15.4":[233],"more":[235],"efficient":[237],"those":[239],"GPU,":[241],"respectively.":[242],"best":[247],"throughput":[249],"efficiency":[252],"among":[253],"other":[254],"state-of-the-art":[255],"platforms":[257],"FPGA,":[259],"even":[260],"targets":[262],"one":[263],"most":[266],"complex":[267],"models.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
