{"id":"https://openalex.org/W4399487529","doi":"https://doi.org/10.1145/3649476.3660391","title":"FFCL: Forward-Forward Net with Cortical Loops, Training and Inference on Edge Without Backpropogation","display_name":"FFCL: Forward-Forward Net with Cortical Loops, Training and Inference on Edge Without Backpropogation","publication_year":2024,"publication_date":"2024-06-10","ids":{"openalex":"https://openalex.org/W4399487529","doi":"https://doi.org/10.1145/3649476.3660391"},"language":"en","primary_location":{"id":"doi:10.1145/3649476.3660391","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649476.3660391","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 Great Lakes Symposium on VLSI 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3649476.3660391","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013688249","display_name":"Ali Karkehabadi","orcid":"https://orcid.org/0000-0001-6075-0880"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ali Karkehabadi","raw_affiliation_strings":["Electrical and Computer Engineering, University of California, Davis, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of California, Davis, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047382437","display_name":"Houman Homayoun","orcid":"https://orcid.org/0000-0001-8904-4699"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Houman Homayoun","raw_affiliation_strings":["University of California, Davis, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Davis, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060036961","display_name":"Avesta Sasan","orcid":"https://orcid.org/0000-0002-4052-8075"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avesta Sasan","raw_affiliation_strings":["University of California, Davis, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Davis, USA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013688249"],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":3.2707,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92228281,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"626","last_page":"632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9932000041007996,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/computer-science","display_name":"Computer science","score":0.6392804980278015},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5782362818717957},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5646361112594604},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.5247987508773804},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5028175711631775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4001636803150177},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23473283648490906},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07392534613609314}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6392804980278015},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5782362818717957},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5646361112594604},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.5247987508773804},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5028175711631775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4001636803150177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23473283648490906},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07392534613609314},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3649476.3660391","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649476.3660391","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 Great Lakes Symposium on VLSI 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3649476.3660391","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649476.3660391","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 Great Lakes Symposium on VLSI 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1547531277","https://openalex.org/W2998249317","https://openalex.org/W3016391357","https://openalex.org/W4287547489","https://openalex.org/W6788404882"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W2912321008","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611"],"abstract_inverted_index":{"The":[0],"Forward-Forward":[1],"Learning":[2],"(FFL)":[3],"algorithm":[4],"is":[5],"a":[6],"recently":[7],"proposed":[8],"solution":[9],"for":[10],"training":[11],"neural":[12],"networks":[13],"without":[14],"needing":[15],"memory-intensive":[16],"backpropagation.":[17],"During":[18],"training,":[19],"labels":[20],"accompany":[21],"input":[22],"data,":[23],"classifying":[24],"them":[25],"as":[26],"positive":[27],"or":[28],"negative":[29],"inputs.":[30],"Each":[31],"layer":[32],"learns":[33],"its":[34],"response":[35],"to":[36,89,101,106],"these":[37],"inputs":[38],"independently.":[39],"In":[40],"this":[41],"study,":[42],"we":[43,72],"enhance":[44,73],"the":[45,48,74,93],"FFL":[46],"with":[47,113],"following":[49],"contributions:":[50],"1)":[51],"We":[52,84],"optimize":[53],"label":[54,58,70],"processing":[55],"by":[56],"segregating":[57],"and":[59,80,99],"feature":[60],"forwarding":[61],"between":[62],"layers,":[63],"enhancing":[64,116],"learning":[65,117],"performance.":[66,82],"2)":[67],"By":[68],"revising":[69],"integration,":[71],"inference":[75],"process,":[76],"reduce":[77],"computational":[78],"complexity,":[79],"improve":[81],"3)":[83],"introduce":[85],"feedback":[86],"loops":[87,91],"akin":[88],"cortical":[90],"in":[92],"brain,":[94],"where":[95],"information":[96],"cycles":[97],"through":[98],"returns":[100],"earlier":[102],"neurons,":[103],"enabling":[104],"layers":[105,112],"combine":[107],"complex":[108],"features":[109],"from":[110],"previous":[111],"lower-level":[114],"features,":[115],"efficiency.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}