{"id":"https://openalex.org/W4409660427","doi":"https://doi.org/10.1109/lascas64004.2025.10966340","title":"An Improved Serial IMPLY Adder Algorithm for Efficient Neural Network Applications","display_name":"An Improved Serial IMPLY Adder Algorithm for Efficient Neural Network Applications","publication_year":2025,"publication_date":"2025-02-25","ids":{"openalex":"https://openalex.org/W4409660427","doi":"https://doi.org/10.1109/lascas64004.2025.10966340"},"language":"en","primary_location":{"id":"doi:10.1109/lascas64004.2025.10966340","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas64004.2025.10966340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 16th Latin America Symposium on Circuits and Systems (LASCAS)","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/A5004122681","display_name":"Fabian Seiler","orcid":"https://orcid.org/0009-0000-6517-451X"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Fabian Seiler","raw_affiliation_strings":["Technische Universität Wien (TU Wien),Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Universität Wien (TU Wien),Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050080301","display_name":"Nima TaheriNejad","orcid":"https://orcid.org/0000-0002-1295-0332"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Nima TaheriNejad","raw_affiliation_strings":["Technische Universität Wien (TU Wien),Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Universität Wien (TU Wien),Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145847075"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.8650000095367432,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T13717","display_name":"Advanced Algorithms and Applications","score":0.8650000095367432,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10320","display_name":"Neural Networks and Applications","score":0.8360999822616577,"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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.7900000214576721,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/adder","display_name":"Adder","score":0.7786862850189209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7708644270896912},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.564311146736145},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4117591381072998},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.39371415972709656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20360514521598816},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08629864454269409},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.08628949522972107}],"concepts":[{"id":"https://openalex.org/C164620267","wikidata":"https://www.wikidata.org/wiki/Q376953","display_name":"Adder","level":3,"score":0.7786862850189209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708644270896912},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.564311146736145},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4117591381072998},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.39371415972709656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20360514521598816},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08629864454269409},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.08628949522972107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lascas64004.2025.10966340","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas64004.2025.10966340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 16th Latin America Symposium on Circuits and Systems (LASCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1578783943","https://openalex.org/W1986623396","https://openalex.org/W2004640050","https://openalex.org/W2025674646","https://openalex.org/W2059441802","https://openalex.org/W2066280488","https://openalex.org/W2081729575","https://openalex.org/W2112181056","https://openalex.org/W2122148191","https://openalex.org/W2162651880","https://openalex.org/W2194775991","https://openalex.org/W2522667948","https://openalex.org/W2542473330","https://openalex.org/W2664249309","https://openalex.org/W2809226946","https://openalex.org/W2809261999","https://openalex.org/W2900379535","https://openalex.org/W2963122961","https://openalex.org/W2972614379","https://openalex.org/W2990351842","https://openalex.org/W3001855497","https://openalex.org/W3002848853","https://openalex.org/W3009643182","https://openalex.org/W3128667769","https://openalex.org/W3136861880","https://openalex.org/W3216738893","https://openalex.org/W4206616727","https://openalex.org/W4211211399","https://openalex.org/W4319865988","https://openalex.org/W4388405402","https://openalex.org/W4392944604","https://openalex.org/W4400770655","https://openalex.org/W4401753018","https://openalex.org/W4404101360","https://openalex.org/W6790967271"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4390550886","https://openalex.org/W3217463396","https://openalex.org/W2516396101","https://openalex.org/W2790557758","https://openalex.org/W3204929712","https://openalex.org/W4295102875","https://openalex.org/W2300671402"],"abstract_inverted_index":{"Memristive":[0],"systems":[1],"are":[2,14,18,30,56],"one":[3],"of":[4,48,127],"the":[5,46,76,82,91,109,124,146,150,170],"most":[6],"promising":[7],"candidates":[8],"for":[9,21,34,58,118,169],"a":[10,69,116,141],"post-CMOS":[11],"era.":[12],"They":[13],"small,":[15],"energy-efficient,":[16],"and":[17,101,130,144,162],"ideal":[19],"targets":[20],"In-Memory":[22],"Computation":[23],"(IMC)":[24],"via":[25],"stateful":[26],"logic.":[27],"As":[28],"adders":[29,54],"critical":[31],"building":[32],"blocks":[33],"any":[35],"computing":[36],"systems,":[37],"improving":[38],"them":[39],"is":[40,62,96,111,156],"an":[41],"essential":[42],"design":[43],"goal.":[44],"With":[45],"rise":[47],"Artificial":[49],"Intelligence":[50],"(AI),":[51],"providing":[52],"memristive":[53],"that":[55,79,121],"optimized":[57],"Neural":[59],"Networks":[60],"(NNs)":[61],"extremely":[63],"important.":[64],"For":[65],"this,":[66],"we":[67],"propose":[68,115],"Material":[70],"Implication":[71],"(IMPLY)-based":[72],"adder":[73,110,139],"algorithm":[74],"in":[75,84,90,140],"serial":[77],"topology":[78],"can":[80,122],"preserve":[81],"weights":[83],"memory,":[85],"which":[86],"was":[87],"not":[88],"addressed":[89],"State-of-the-Art":[92],"(SoA).":[93],"Our":[94,154],"approach":[95,155],"20%":[97,166],"-":[98,104],"23%":[99],"faster":[100],"requires":[102,163],"1%":[103],"12%":[105],"less":[106],"energy":[107],"when":[108],"used":[112],"repeatedly.":[113],"We":[114,136],"flowchart":[117],"IMPLY-based":[119],"algorithms":[120],"represent":[123],"state":[125],"changes":[126],"individual":[128],"memristors":[129],"apply":[131],"it":[132],"to":[133,158,165],"our":[134,138],"adder.":[135,174],"embed":[137],"shift-and-add":[142],"multiplier":[143],"evaluate":[145],"potential":[147],"gains":[148],"on":[149],"8-bit":[151],"quantized":[152],"ResNet18.":[153],"up":[157,164],"17%":[159],"more":[160],"energy-efficient":[161],"fewer":[167],"cycles":[168],"inference":[171],"than":[172],"SoA":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}