{"id":"https://openalex.org/W7166323374","doi":"https://doi.org/10.1145/3801487.3801834","title":"HMix : An Efficient Hardware Accelerator for Quantized MLP-Mixer Inference","display_name":"HMix : An Efficient Hardware Accelerator for Quantized MLP-Mixer Inference","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7166323374","doi":"https://doi.org/10.1145/3801487.3801834"},"language":null,"primary_location":{"id":"doi:10.1145/3801487.3801834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3801487.3801834","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139466336","display_name":"Dhananjay Rao Thallikar Shyam","orcid":"https://orcid.org/0009-0000-1565-1787"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dhananjay Rao Thallikar Shyam","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":"https://orcid.org/0009-0000-1565-1787","affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079937406","display_name":"Shashank Nag","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shashank Nag","raw_affiliation_strings":["The University of Texas at Austin, Austin, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0000-3619-6066","affiliations":[{"raw_affiliation_string":"The University of Texas at Austin, Austin, Texas, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068885069","display_name":"Lizy K. John","orcid":"https://orcid.org/0000-0002-8747-5214"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lizy K. John","raw_affiliation_strings":["The University of Texas at Austin, Austin, Texas, USA"],"raw_orcid":"https://orcid.org/0000-0002-8747-5214","affiliations":[{"raw_affiliation_string":"The University of Texas at Austin, Austin, Texas, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"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":0.93140011,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"201","last_page":"209"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6194000244140625,"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":0.6194000244140625,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.08250000327825546,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.048900000751018524,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5924000144004822},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5450999736785889},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49219998717308044},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.4652000069618225},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.4043999910354614},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3995000123977661},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.39890000224113464},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.35659998655319214},{"id":"https://openalex.org/keywords/random-access-memory","display_name":"Random access memory","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7195000052452087},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5924000144004822},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4641999900341034},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.39890000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C2994168587","wikidata":"https://www.wikidata.org/wiki/Q5295","display_name":"Random access memory","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.35019999742507935},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.3425999879837036},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C142962650","wikidata":"https://www.wikidata.org/wiki/Q240838","display_name":"Reconfigurable computing","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.29919999837875366},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29089999198913574},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2745000123977661},{"id":"https://openalex.org/C65232700","wikidata":"https://www.wikidata.org/wiki/Q5656403","display_name":"Hardware architecture","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C812465","wikidata":"https://www.wikidata.org/wiki/Q5058375","display_name":"Cellular neural network","level":3,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3801487.3801834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3801487.3801834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4125345051288605,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G5263952094","display_name":"Fuse2: Topic 1: Efficient Edge Inference and Heterogeneous Integration in Systems for Health and Chemical Sensing","funder_award_id":"2425655","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6138234132","display_name":"Collaborative Research: SHF: Small: Quasi Weightless Neural Networks for Energy-Efficient Machine Learning on the Edge","funder_award_id":"2326894","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2017369466","https://openalex.org/W2117539524","https://openalex.org/W2289252105","https://openalex.org/W2945146780","https://openalex.org/W2964133305","https://openalex.org/W3006300330","https://openalex.org/W3138516171","https://openalex.org/W4205172069","https://openalex.org/W4221072574","https://openalex.org/W4247470470","https://openalex.org/W4289656095","https://openalex.org/W4308479898","https://openalex.org/W4321637298","https://openalex.org/W4380874786","https://openalex.org/W4385187240","https://openalex.org/W4386422402","https://openalex.org/W4387789586","https://openalex.org/W4393973515","https://openalex.org/W4402829565","https://openalex.org/W4405389775","https://openalex.org/W4407827689","https://openalex.org/W4416336843"],"related_works":[],"abstract_inverted_index":{"Computer":[0],"vision":[1,35],"workloads":[2],"are":[3],"increasingly":[4],"deployed":[5],"at":[6],"the":[7,37,52],"edge,":[8],"driving":[9],"demand":[10],"for":[11,74],"efficient,":[12],"reconfigurable":[13],"accelerators":[14],"that":[15,70],"balance":[16],"accuracy,":[17],"performance,":[18],"resource":[19],"utilization,":[20],"and":[21,29,55],"power":[22],"consumption.":[23],"While":[24],"Convolutional":[25],"Neural":[26],"Networks":[27],"(CNNs)":[28],"Vision":[30],"Transformers":[31],"(ViTs)":[32],"dominate":[33],"current":[34],"pipelines,":[36],"MLP-Mixer":[38,59],"is":[39],"a":[40],"deviation":[41],"by":[42],"utilizing":[43],"an":[44],"all-MLP":[45],"architecture":[46],"while":[47],"achieving":[48],"competitive":[49],"accuracy.":[50],"However,":[51],"alternating":[53],"Token-":[54],"Channel-mixing":[56],"structure":[57],"of":[58],"introduces":[60],"frequent":[61],"tensor":[62],"transpositions,":[63],"resulting":[64],"in":[65],"irregular":[66],"memory":[67],"access":[68],"patterns":[69],"pose":[71],"significant":[72],"challenges":[73],"efficient":[75],"hardware":[76],"implementation,":[77],"particularly":[78],"on":[79],"resource-constrained":[80],"edge":[81],"platforms.":[82]},"counts_by_year":[],"updated_date":"2026-06-28T06:20:29.262167","created_date":"2026-06-28T00:00:00"}