{"id":"https://openalex.org/W4414166002","doi":"https://doi.org/10.1109/jssc.2025.3604814","title":"ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping","display_name":"ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping","publication_year":2025,"publication_date":"2025-09-09","ids":{"openalex":"https://openalex.org/W4414166002","doi":"https://doi.org/10.1109/jssc.2025.3604814"},"language":"en","primary_location":{"id":"doi:10.1109/jssc.2025.3604814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jssc.2025.3604814","pdf_url":null,"source":{"id":"https://openalex.org/S83637746","display_name":"IEEE Journal of Solid-State Circuits","issn_l":"0018-9200","issn":["0018-9200","1558-173X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Solid-State Circuits","raw_type":"journal-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/A5084915481","display_name":"Heetak Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heetak Kim","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091662625","display_name":"Yunpyo Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yunpyo Hong","raw_affiliation_strings":["Korea Electronics Technology Institute, Gyeonggi, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Gyeonggi, South Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642851","display_name":"Byung\u2010Soo Kim","orcid":"https://orcid.org/0000-0001-5210-7430"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byungsoo Kim","raw_affiliation_strings":["Korea Electronics Technology Institute, Gyeonggi, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Gyeonggi, South Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101839916","display_name":"Jongsun Park","orcid":"https://orcid.org/0000-0003-3251-0024"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongsun Park","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-3251-0024","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20008588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"61","issue":"5","first_page":"2379","last_page":"2390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9851999878883362,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9851999878883362,"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":0.9837999939918518,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.531000018119812},{"id":"https://openalex.org/keywords/dataflow","display_name":"Dataflow","score":0.5281999707221985},{"id":"https://openalex.org/keywords/lookup-table","display_name":"Lookup table","score":0.5180000066757202},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5078999996185303},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5041999816894531},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.49000000953674316},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4814000129699707},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48030000925064087},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.43799999356269836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7710999846458435},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.5281999707221985},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5259000062942505},{"id":"https://openalex.org/C134835016","wikidata":"https://www.wikidata.org/wiki/Q690265","display_name":"Lookup table","level":2,"score":0.5180000066757202},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.49000000953674316},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4814000129699707},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.44359999895095825},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.43799999356269836},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C184896649","wikidata":"https://www.wikidata.org/wiki/Q290066","display_name":"Routing table","level":4,"score":0.3334999978542328},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.2815999984741211},{"id":"https://openalex.org/C142962650","wikidata":"https://www.wikidata.org/wiki/Q240838","display_name":"Reconfigurable computing","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C76752949","wikidata":"https://www.wikidata.org/wiki/Q7607499","display_name":"Stencil","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jssc.2025.3604814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jssc.2025.3604814","pdf_url":null,"source":{"id":"https://openalex.org/S83637746","display_name":"IEEE Journal of Solid-State Circuits","issn_l":"0018-9200","issn":["0018-9200","1558-173X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Solid-State Circuits","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5582485663","display_name":null,"funder_award_id":"RS-2024-00345481","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G803298781","display_name":null,"funder_award_id":"P0028486","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-skipping":[0],"is":[1,19,85,146,151],"a":[2,70,113,141,154,168],"famous":[3],"technique":[4],"to":[5,34,153],"improve":[6,125],"the":[7,17,126],"energy":[8],"efficiency":[9],"of":[10,31,48,180],"deep":[11],"neural":[12],"network":[13],"(DNN)":[14],"accelerators.":[15],"When":[16],"zero-skipping":[18,93,109],"realized":[20],"with":[21,87],"encoded":[22],"data":[23,32,49],"using":[24],"lossless":[25],"compression,":[26],"irregular":[27],"and":[28,59,111,171,190],"unpredictable":[29,61],"size":[30],"due":[33],"inconsistent":[35],"compression":[36],"rate":[37],"incurs":[38],"several":[39],"design":[40],"issues":[41],"including:":[42],"1)":[43,77],"load":[44],"imbalance":[45],"from":[46],"irregularity":[47],"stored":[50],"in":[51,97,167],"buffers;":[52],"2)":[53,103],"complex":[54],"routing":[55],"for":[56,94],"accumulation":[57],"process;":[58],"3)":[60,112],"memory":[62],"footprint":[63],"allocation.":[64],"In":[65],"this":[66],"article,":[67],"we":[68],"propose":[69],"DNN":[71,175],"accelerator":[72],"named":[73],"ROZK,":[74],"which":[75],"includes:":[76],"tri-stationary":[78],"dataflow":[79],"where":[80],"each":[81],"processing":[82],"element":[83],"(PE)":[84],"equipped":[86],"local":[88,98,108],"register":[89],"files":[90],"that":[91,118],"enable":[92],"unstructured":[95],"zeros":[96],"PEs":[99],"without":[100],"encoding":[101],"data;":[102],"low-cost":[104],"lookup":[105],"table":[106],"(LUT)-based":[107],"scheme;":[110],"reconfigurable":[114],"network-on-chip":[115],"(NoC)":[116],"architecture":[117,163],"supports":[119],"various":[120,174],"stationary":[121],"types.":[122],"To":[123],"further":[124],"computation":[127],"utilization":[128],"by":[129],"transferring":[130],"inputs":[131],"as":[132,134],"soon":[133],"every":[135],"computing":[136],"unit":[137],"finishes":[138],"its":[139],"computations,":[140],"cycle":[142],"prediction":[143],"scheduler":[144],"(CPS)":[145],"also":[147],"proposed.":[148],"Finally,":[149],"ROZK":[150,177],"connected":[152],"64-bit":[155],"RISC-V":[156],"core-based":[157],"system-on-chip":[158],"(SoC)":[159],"architecture.":[160],"The":[161],"SoC":[162],"has":[164],"been":[165],"fabricated":[166],"28-nm":[169],"process":[170],"verified":[172],"through":[173],"workloads.":[176],"achieves":[178],"throughput":[179],"191":[181],"giga":[182],"operations":[183],"per":[184],"second":[185],"(GOPS)":[186],"(0%":[187],"activation":[188,194],"sparsity)":[189,195],"324":[191],"GOPS":[192],"(60%":[193],"at":[196],"409-MHz":[197],"operating":[198],"frequency.":[199]},"counts_by_year":[],"updated_date":"2026-04-25T06:01:18.069262","created_date":"2025-10-10T00:00:00"}