{"id":"W4403446571","doi":"10.1109/tbcas.2024.3481160","title":"BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Neuromorphic engineering; Computer science; Multiplier (economics); State (computer science); Computer hardware; Coprocessor; Bit (key); Parallel computing; Arithmetic; Electronic engineering; Artificial intelligence; Algorithm; Artificial neural network; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001384672,0.0004238745,0.0002231954,0.0003724985,0.0002475957,0.0003973684,0.0009316768,0.0003140225,0.007019423],"category_scores_gemma":[0.0004657873,0.0001596032,0.000204613,0.000357662,0.0001658026,0.0006824869,0.0002934092,0.0003793018,0.001210755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000420452,"about_ca_system_score_gemma":0.0006798821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001725721,"about_ca_topic_score_gemma":0.006267407,"domain_scores_codex":[0.9998941,0.000009743948,0.000007477551,0.00002694496,0.00003966879,0.00002198756],"domain_scores_gemma":[0.9998877,0.00002567742,0.00001996482,0.00001463404,0.00004030644,0.00001180385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006856991,0.0003326766,0.003298493,0.0003577632,0.0001236388,0.0003981409,0.000120531,0.05256162,0.1715271,0.00941462,0.03508302,0.7260967],"study_design_scores_gemma":[0.0001608688,0.001307731,0.003904872,0.0001107157,0.0001347372,0.001271619,0.00009043462,0.7722282,0.1636737,0.01122861,0.04580236,0.00008623012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2414411,0.001998454,0.7223024,0.0009028455,0.0004551189,0.0002634382,0.001082673,0.01074101,0.02081302],"genre_scores_gemma":[0.8036863,0.0003837969,0.1799776,0.0005372754,0.00005438547,0.0001389314,0.001009341,0.0001681331,0.01404417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007019423,"threshold_uncertainty_score":0.02348232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03799952034486521,"score_gpt":0.2446836631765777,"score_spread":0.2066841428317125,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}