{"id":"W4387459935","doi":"10.1039/bk9781839169946-00680","title":"Memristive Devices for Neuromorphic and Deep Learning Applications","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Neuromorphic engineering; Memristor; Computer architecture; Implementation; Computer science; Computer engineering; Electronic engineering; Artificial intelligence; Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001096874,0.0007280025,0.0003155464,0.001019251,0.0004047986,0.001274882,0.0008589419,0.0008538703,0.03278892],"category_scores_gemma":[0.0002841634,0.0003928639,0.000369578,0.001025081,0.0003615141,0.001785618,0.0007170236,0.001711556,0.01163983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003989765,"about_ca_system_score_gemma":0.0002896625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002119009,"about_ca_topic_score_gemma":0.0006715248,"domain_scores_codex":[0.9998996,0.000006438002,0.000004848771,0.00001871645,0.00006160501,0.000008707528],"domain_scores_gemma":[0.999923,0.00002987626,0.000006076455,0.00001007993,0.00002348555,0.000007490486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005080312,0.00008629796,0.0001357462,0.001810092,0.00002975009,0.0002587076,0.0002544529,0.002948287,0.04086057,0.1881462,0.1169544,0.6484646],"study_design_scores_gemma":[0.000006436161,0.00004746933,0.0001873246,0.0005357016,0.00001363414,0.0006086624,0.00005217685,0.004673464,0.01009561,0.04333721,0.940423,0.00001940824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.007608776,0.2511564,0.1573191,0.003810014,0.005436072,0.0002010437,0.0008825597,0.001979803,0.5716063],"genre_scores_gemma":[0.04749208,0.1427262,0.09889425,0.002234983,0.001472092,0.0002241464,0.0006674342,0.0004631025,0.7058257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03278892,"threshold_uncertainty_score":0.1096899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798666797974482,"score_gpt":0.2384113170199525,"score_spread":0.2004246490402077,"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."}}