{"id":"https://openalex.org/W4311839462","doi":"https://doi.org/10.1109/icfpt56656.2022.9974543","title":"LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning","display_name":"LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning","publication_year":2022,"publication_date":"2022-12-05","ids":{"openalex":"https://openalex.org/W4311839462","doi":"https://doi.org/10.1109/icfpt56656.2022.9974543"},"language":"en","primary_location":{"id":"doi:10.1109/icfpt56656.2022.9974543","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfpt56656.2022.9974543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Field-Programmable Technology (ICFPT)","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/A5088133775","display_name":"JaeUk Kim","orcid":null},"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":"JaeUk Kim","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":0.2653,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6240312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9944999814033508,"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":0.9944999814033508,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9937000274658203,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9932000041007996,"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/computer-science","display_name":"Computer science","score":0.7952478528022766},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7421119213104248},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6529062986373901},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.532477617263794},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5139772295951843},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4888398051261902},{"id":"https://openalex.org/keywords/stratix","display_name":"Stratix","score":0.48694002628326416},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.4732266664505005},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.41219162940979004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4118078649044037},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.2662436366081238}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7952478528022766},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7421119213104248},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6529062986373901},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.532477617263794},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5139772295951843},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4888398051261902},{"id":"https://openalex.org/C2776277307","wikidata":"https://www.wikidata.org/wiki/Q22074755","display_name":"Stratix","level":3,"score":0.48694002628326416},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.4732266664505005},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.41219162940979004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4118078649044037},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.2662436366081238},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icfpt56656.2022.9974543","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfpt56656.2022.9974543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Field-Programmable Technology (ICFPT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1641379095","https://openalex.org/W2063054322","https://openalex.org/W2119144962","https://openalex.org/W2145339207","https://openalex.org/W2285660444","https://openalex.org/W2602275733","https://openalex.org/W2767785892","https://openalex.org/W2787938642","https://openalex.org/W2794952988","https://openalex.org/W2920954974","https://openalex.org/W2931767035","https://openalex.org/W2945146780","https://openalex.org/W2949866178","https://openalex.org/W2963674932","https://openalex.org/W2963864421","https://openalex.org/W2964043796","https://openalex.org/W2965862350","https://openalex.org/W2979439447","https://openalex.org/W2982316857","https://openalex.org/W2990172850","https://openalex.org/W3016832937","https://openalex.org/W3040646053","https://openalex.org/W3088811935","https://openalex.org/W3091492359","https://openalex.org/W3092357178","https://openalex.org/W3105802176","https://openalex.org/W3121197820","https://openalex.org/W3132616766","https://openalex.org/W3163275603","https://openalex.org/W3169326189","https://openalex.org/W3183385172","https://openalex.org/W3213742958","https://openalex.org/W3214241811","https://openalex.org/W4236868170","https://openalex.org/W4240168186","https://openalex.org/W4289100326","https://openalex.org/W4295846245","https://openalex.org/W4297785866","https://openalex.org/W4298857966","https://openalex.org/W4299802797","https://openalex.org/W4300830895","https://openalex.org/W6637967152","https://openalex.org/W6638632666","https://openalex.org/W6677580257","https://openalex.org/W6684921986","https://openalex.org/W6692846177","https://openalex.org/W6731193076","https://openalex.org/W6735650757","https://openalex.org/W6738796088","https://openalex.org/W6740217080","https://openalex.org/W6746331415","https://openalex.org/W6748839928","https://openalex.org/W6757784512","https://openalex.org/W6782869744","https://openalex.org/W6788396906","https://openalex.org/W6803572779"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W1973046741","https://openalex.org/W2983282793","https://openalex.org/W1988651200","https://openalex.org/W2052993554","https://openalex.org/W2991370896","https://openalex.org/W2046125858","https://openalex.org/W2106942255","https://openalex.org/W2891818448"],"abstract_inverted_index":{"Multi-agent":[0],"reinforcement":[1,46],"learning":[2,47],"(MARL)":[3],"is":[4,37,227],"a":[5,59,89,143],"powerful":[6],"technology":[7],"to":[8,123,159,195,215],"construct":[9],"interactive":[10,52],"artificial":[11],"intelligent":[12],"systems":[13],"in":[14,44,178],"various":[15,176],"applications":[16],"such":[17],"as":[18],"multi-robot":[19],"control":[20],"and":[21,51,93,119,151,161,170,184,200],"self-driving":[22],"cars.":[23],"Unlike":[24],"supervised":[25],"model":[26],"or":[27],"single-agent":[28],"rein-forcement":[29],"learning,":[30],"which":[31,67,180,226],"actively":[32],"exploits":[33],"network":[34,69],"pruning,":[35],"it":[36],"obscure":[38],"that":[39,101],"how":[40],"pruning":[41,70],"will":[42],"work":[43],"multi-agent":[45],"with":[48,80,105,138],"its":[49],"cooperative":[50],"characteristics.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57],"present":[58],"real-time":[60],"sparse":[61,96,131,155,220,232],"training":[62,73,199,233],"accel-eration":[63],"system":[64,146],"named":[65],"LearningGroup,":[66],"adopts":[68],"on":[71,109],"the":[72,77,110,134,148,196,210,223,228],"of":[74,133],"MARL":[75],"for":[76,154,175,218],"first":[78],"time":[79,150],"an":[81],"algorithm/architecture":[82],"co-design":[83],"approach.":[84],"We":[85],"create":[86],"spar-sity":[87],"using":[88],"weight":[90,117,135],"grouping":[91],"algorithm":[92],"propose":[94],"on-chip":[95,198],"data":[97,156,221],"encoding":[98,104,112],"loop":[99],"(OSEL)":[100],"enables":[102],"fast":[103],"efficient":[106,116,188],"implementation.":[107],"Based":[108],"OSEL's":[111],"format,":[113],"LearningGroup":[114,145],"performs":[115],"compression":[118],"computation":[120],"workload":[121],"allocation":[122],"multiple":[124,130],"cores,":[125],"where":[126],"each":[127],"core":[128],"handles":[129],"rows":[132],"matrix":[136],"simultaneously":[137],"vector":[139],"processing":[140,219],"units.":[141],"As":[142],"result,":[144],"minimizes":[147],"cycle":[149],"memory":[152],"footprint":[153],"generation":[157],"up":[158,214],"5.72x":[160],"6.81x.":[162],"Its":[163],"FPGA":[164],"accelerator":[165,211],"shows":[166,212],"257.40-3629.48":[167],"GFLOPS":[168],"throughput":[169],"7.10-100.12":[171],"GFLOPS/W":[172],"energy":[173,187],"efficiency":[174],"conditions":[177],"MARL,":[179],"are":[181],"7.13x":[182],"higher":[183],"12.43x":[185],"more":[186],"than":[189],"Nvidia":[190],"Titan":[191],"RTX":[192],"GPU,":[193],"thanks":[194],"fully":[197],"highly":[201],"optimized":[202],"dataflow/data":[203],"format":[204],"provided":[205],"by":[206],"FPGA.":[207],"Most":[208],"importantly,":[209],"speedup":[213],"12.52":[216],"x":[217],"over":[222],"dense":[224],"case,":[225],"highest":[229],"among":[230],"state-of-the-art":[231],"accelerators.":[234]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
