{"id":"W2799818742","doi":"10.1109/iscas.2018.8351110","title":"Hardware Realization of Mixed-Signal Neural Networks with Modular Synapse-Neuron arrays","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Synapse; Artificial neural network; Computer science; Modular design; SIGNAL (programming language); Realization (probability); Neuron; Topology (electrical circuits); Computer hardware; Artificial intelligence; Electrical engineering; Mathematics; Neuroscience; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000450437,0.0001215425,0.0001324014,0.00003587884,0.00006009378,0.000009078621,0.0000867702,0.00004130228,0.00003752585],"category_scores_gemma":[0.000006476389,0.0001008499,0.00002514291,0.0001693846,0.0000460797,0.0001359161,0.00001938089,0.0001049345,0.000002626058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001099951,"about_ca_system_score_gemma":0.000002845926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002394939,"about_ca_topic_score_gemma":0.000006986112,"domain_scores_codex":[0.9993978,0.00002036403,0.0001537198,0.0001424625,0.00009438822,0.0001913094],"domain_scores_gemma":[0.9996822,0.00002767326,0.00003406472,0.000153073,0.00005601854,0.00004692007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002172692,0.000005159782,0.0002044269,0.00002538321,0.000009836601,0.000005503127,0.00003552029,0.9751575,0.02150697,0.0003030043,0.0001520623,0.00257292],"study_design_scores_gemma":[0.0001679399,0.000190295,0.0006980703,0.00002930018,0.000009884149,0.00001674145,0.0000177674,0.8558549,0.142726,0.00005810195,0.00009992033,0.0001311438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4156028,0.00002879088,0.583351,0.00000433765,0.0001293077,0.0000615046,8.449726e-7,0.0001949749,0.0006264081],"genre_scores_gemma":[0.9985808,0.000006636428,0.001079957,0.00003613155,0.0002132782,0.000002043539,0.000008993231,0.0000289712,0.00004320083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.582978,"threshold_uncertainty_score":0.4112539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011889304583496,"score_gpt":0.2040556374286547,"score_spread":0.1939367443828197,"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."}}