{"id":"https://openalex.org/W4411725016","doi":"https://doi.org/10.1109/iscas56072.2025.11044290","title":"A Hybrid-Domain Floating-Point Compute-In-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks","display_name":"A Hybrid-Domain Floating-Point Compute-In-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks","publication_year":2025,"publication_date":"2025-05-25","ids":{"openalex":"https://openalex.org/W4411725016","doi":"https://doi.org/10.1109/iscas56072.2025.11044290"},"language":"en","primary_location":{"id":"doi:10.1109/iscas56072.2025.11044290","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas56072.2025.11044290","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5108326780","display_name":"Z. Y. Yi","orcid":"https://orcid.org/0009-0001-7288-498X"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqiang Yi","raw_affiliation_strings":["The George Washington University,Department of Electrical and Computer Engineering,DC,US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Department of Electrical and Computer Engineering,DC,US","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006343148","display_name":"Yiwen Liang","orcid":"https://orcid.org/0000-0002-8297-5009"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiwen Liang","raw_affiliation_strings":["The George Washington University,Department of Electrical and Computer Engineering,DC,US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Department of Electrical and Computer Engineering,DC,US","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062801966","display_name":"Weidong Cao","orcid":"https://orcid.org/0000-0001-7539-8250"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weidong Cao","raw_affiliation_strings":["The George Washington University,Department of Electrical and Computer Engineering,DC,US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University,Department of Electrical and Computer Engineering,DC,US","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":3.0301,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89626391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9690999984741211,"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"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9690999984741211,"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"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9567000269889832,"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/T10320","display_name":"Neural Networks and Applications","score":0.9509999752044678,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7426638603210449},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.6192628145217896},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5526847243309021},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5344570875167847},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5312908291816711},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.4981203079223633},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4788134694099426},{"id":"https://openalex.org/keywords/parallel-architecture","display_name":"Parallel architecture","score":0.42423659563064575},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41858065128326416},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3486202359199524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2971951961517334},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24975323677062988},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07940429449081421},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07939460873603821}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7426638603210449},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.6192628145217896},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5526847243309021},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5344570875167847},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5312908291816711},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.4981203079223633},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4788134694099426},{"id":"https://openalex.org/C2985918086","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel architecture","level":3,"score":0.42423659563064575},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41858065128326416},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3486202359199524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2971951961517334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24975323677062988},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07940429449081421},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07939460873603821},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"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/iscas56072.2025.11044290","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas56072.2025.11044290","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W3015432327","https://openalex.org/W3033443178","https://openalex.org/W3048705738","https://openalex.org/W3133754064","https://openalex.org/W3190062760","https://openalex.org/W3211168157","https://openalex.org/W4220958508","https://openalex.org/W4221101426","https://openalex.org/W4296704900","https://openalex.org/W4360605483","https://openalex.org/W4360606939","https://openalex.org/W4384947654","https://openalex.org/W4387092567","https://openalex.org/W4388692445","https://openalex.org/W4389166767","https://openalex.org/W4392746496","https://openalex.org/W4404133681"],"related_works":["https://openalex.org/W2565094479","https://openalex.org/W2390829436","https://openalex.org/W1989791859","https://openalex.org/W602859758","https://openalex.org/W1971289376","https://openalex.org/W2379101322","https://openalex.org/W1992553864","https://openalex.org/W2158228144","https://openalex.org/W206792560","https://openalex.org/W1965117583"],"abstract_inverted_index":{"Compute-In-memory":[0],"(CIM)":[1],"has":[2,27],"shown":[3],"significant":[4],"potential":[5],"in":[6,17,31],"efficiently":[7,83],"accelerating":[8],"deep":[9],"neural":[10],"networks":[11],"(DNNs)":[12],"at":[13],"the":[14,38,78,105,113],"edge,":[15],"particularly":[16],"speeding":[18],"up":[19],"quantized":[20],"models":[21],"for":[22],"inference":[23,46],"applications.":[24],"Recently,":[25],"there":[26],"been":[28],"growing":[29],"interest":[30],"developing":[32],"floating-point-based":[33],"CIM":[34,69,76],"macros":[35],"to":[36,59,82,97],"improve":[37],"accuracy":[39,111],"of":[40,112],"high-precision":[41,85],"DNN":[42],"models,":[43],"including":[44],"both":[45],"and":[47,74,92,109],"training":[48],"tasks.":[49],"Yet,":[50],"current":[51],"implementations":[52],"rely":[53],"primarily":[54],"on":[55,116],"digital":[56,75],"methods,":[57],"leading":[58],"substantial":[60],"power":[61],"consumption.":[62],"This":[63],"paper":[64],"introduces":[65],"a":[66],"hybrid":[67],"domain":[68],"architecture":[70],"that":[71],"integrates":[72],"analog":[73],"within":[77],"same":[79],"memory":[80],"cell":[81],"accelerate":[84],"DNNs.":[86],"Specifically,":[87],"we":[88],"develop":[89],"area-efficient":[90],"circuits":[91],"energy-efficient":[93],"analog-to-digital":[94],"conversion":[95],"techniques":[96],"realize":[98],"this":[99],"architecture.":[100],"Comprehensive":[101],"circuit-level":[102],"simulations":[103],"reveal":[104],"notable":[106],"energy":[107],"efficiency":[108],"lossless":[110],"proposed":[114],"design":[115],"benchmarks.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}