{"id":"https://openalex.org/W4403582494","doi":"https://doi.org/10.1145/3627673.3679603","title":"Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors","display_name":"Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582494","doi":"https://doi.org/10.1145/3627673.3679603"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679603","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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3627673.3679603","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046844612","display_name":"Matt Gorbett","orcid":"https://orcid.org/0000-0002-5000-1242"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matt Gorbett","raw_affiliation_strings":["Colorado State University, Fort Collins, CO, USA"],"raw_orcid":"https://orcid.org/0000-0002-5000-1242","affiliations":[{"raw_affiliation_string":"Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047638523","display_name":"Hossein Shirazi","orcid":"https://orcid.org/0000-0002-2721-0628"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hossein Shirazi","raw_affiliation_strings":["San Diego State University, San Diego, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-2721-0628","affiliations":[{"raw_affiliation_string":"San Diego State University, San Diego, CA, USA","institution_ids":["https://openalex.org/I26538001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008904412","display_name":"Indrakshi Ray","orcid":"https://orcid.org/0000-0002-0714-7676"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Indrakshi Ray","raw_affiliation_strings":["Colorado State University, Fort Collins, CO, USA"],"raw_orcid":"https://orcid.org/0000-0002-0714-7676","affiliations":[{"raw_affiliation_string":"Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.15376164,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"674","last_page":"684"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9979000091552734,"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/T10320","display_name":"Neural Networks and Applications","score":0.9979000091552734,"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":0.9979000091552734,"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/T13182","display_name":"Quantum-Dot Cellular Automata","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/bit","display_name":"Bit (key)","score":0.7722885608673096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6875178813934326},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5906832814216614},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5535736083984375},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5219019055366516},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4595557451248169},{"id":"https://openalex.org/keywords/bit-array","display_name":"Bit array","score":0.4581195116043091},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.4569533169269562},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.44021567702293396},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.352843701839447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23306241631507874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19572663307189941},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1365295648574829},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09512192010879517},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06009605526924133}],"concepts":[{"id":"https://openalex.org/C117011727","wikidata":"https://www.wikidata.org/wiki/Q1278488","display_name":"Bit (key)","level":2,"score":0.7722885608673096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6875178813934326},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5906832814216614},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5535736083984375},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5219019055366516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4595557451248169},{"id":"https://openalex.org/C150807984","wikidata":"https://www.wikidata.org/wiki/Q1992074","display_name":"Bit array","level":3,"score":0.4581195116043091},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4569533169269562},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.44021567702293396},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.352843701839447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23306241631507874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19572663307189941},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1365295648574829},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09512192010879517},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06009605526924133},{"id":"https://openalex.org/C25197100","wikidata":"https://www.wikidata.org/wiki/Q890886","display_name":"Drilling","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679603","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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627673.3679603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679603","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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2194775991","https://openalex.org/W2300242332","https://openalex.org/W2624696420","https://openalex.org/W2883780447","https://openalex.org/W2947515409","https://openalex.org/W2954698171","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W2963521187","https://openalex.org/W2998218113","https://openalex.org/W3001665736","https://openalex.org/W3008515144","https://openalex.org/W3034234149","https://openalex.org/W3034297393","https://openalex.org/W3044604993","https://openalex.org/W3134556198","https://openalex.org/W3138516171","https://openalex.org/W3176840244","https://openalex.org/W3177318507","https://openalex.org/W3188872815","https://openalex.org/W3195438473","https://openalex.org/W3207102667","https://openalex.org/W3213568228","https://openalex.org/W4213061003","https://openalex.org/W4214888034","https://openalex.org/W4234552385","https://openalex.org/W4281758439","https://openalex.org/W4282581586","https://openalex.org/W4283766928","https://openalex.org/W4285048954","https://openalex.org/W4288346093","https://openalex.org/W4312376588","https://openalex.org/W4385565515","https://openalex.org/W4386071500","https://openalex.org/W4390873188","https://openalex.org/W6780827055","https://openalex.org/W6838539104"],"related_works":["https://openalex.org/W51097961","https://openalex.org/W2257409576","https://openalex.org/W4298276541","https://openalex.org/W2021689768","https://openalex.org/W2612632602","https://openalex.org/W2321805087","https://openalex.org/W1656395319","https://openalex.org/W2952523897","https://openalex.org/W4243615267","https://openalex.org/W3180134152"],"abstract_inverted_index":{"Binary":[0],"Neural":[1],"Networks":[2],"(BNNs)":[3],"enable":[4],"efficient":[5],"deep":[6],"learning":[7],"by":[8],"saving":[9],"on":[10,123],"storage":[11],"and":[12,75,101,132,136,175],"computational":[13,26],"costs.":[14],"However,":[15],"as":[16],"the":[17,81,91,96,107,116,163,183],"size":[18,147],"of":[19,40,49,55,70,109,127,185],"neural":[20,44,57,113],"networks":[21],"continues":[22],"to":[23,42,51,66,89,98,142,149,165,168,181],"grow,":[24],"meeting":[25],"require-":[27],"ments":[28],"remains":[29],"a":[30,37,71,84,124,166,177,186],"challenge.":[31],"In":[32],"this":[33],"work,":[34],"we":[35,161],"propose":[36],"new":[38],"form":[39],"quantization":[41],"tile":[43,86,188],"network":[45],"layers":[46],"with":[47,140],"sequences":[48],"bits":[50],"achieve":[52],"sub-bit":[53],"compression":[54],"binary-weighted":[56,150],"networks.":[58],"The":[59],"method":[60,82],"learns":[61],"binary":[62],"vectors":[63],"(i.e.":[64],"tiles)":[65],"populate":[67],"each":[68],"layer":[69,88,190],"model":[72,164],"via":[73],"aggregation":[74],"reshaping":[76],"operations.":[77],"During":[78],"in-":[79],"ference,":[80],"reuses":[83],"single":[85,187],"per":[87,189],"represent":[90],"full":[92],"tensor.":[93],"We":[94,152],"employ":[95],"approach":[97,117],"both":[99],"fully-connected":[100],"convolutional":[102],"layers,":[103],"which":[104],"make":[105],"up":[106,141],"breadth":[108],"space":[110],"in":[111,146,172,191],"most":[112],"architectures.":[114],"Empirically,":[115],"achieves":[118],"near":[119],"full-":[120],"precision":[121],"performance":[122],"diverse":[125],"range":[126],"architectures":[128],"(CNNs,":[129],"Transformers,":[130],"MLPs)":[131],"tasks":[133],"(classification,":[134],"segmentation,":[135],"time":[137],"series":[138],"forecasting)":[139],"an":[143],"8x":[144],"reduction":[145],"compared":[148],"models.":[151],"provide":[153],"two":[154],"implementations":[155],"for":[156],"Tiled":[157],"Bit":[158],"Networks:":[159],"1)":[160],"deploy":[162],"microcontroller":[167],"assess":[169],"its":[170],"feasibility":[171],"resource-constrained":[173],"environments,":[174],"2)":[176],"GPU-compatible":[178],"inference":[179],"kernel":[180],"facilitate":[182],"reuse":[184],"memory.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
