{"id":"W2128587608","doi":"10.1109/isscc.2003.1234309","title":"A current-saving match-line sensing scheme for content-addressable memories","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Scheme (mathematics); Line (geometry); Content-addressable storage; Power consumption; Computer science; Power (physics); CMOS; Content-addressable memory; Computer hardware; Electronic engineering; Electrical engineering; Engineering; Artificial intelligence; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001238447,0.0001596282,0.0001856748,0.00003958003,0.0001346562,0.00003445137,0.00005773213,0.00003598682,0.00003388055],"category_scores_gemma":[0.0001317484,0.0001518052,0.00006376483,0.0001116554,0.00001882774,0.000175171,0.0000166771,0.0001376262,0.00001677397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002907327,"about_ca_system_score_gemma":0.000007963457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.280235e-7,"about_ca_topic_score_gemma":0.000003889721,"domain_scores_codex":[0.9991825,0.00001184982,0.0002131321,0.0001699116,0.00007630559,0.000346348],"domain_scores_gemma":[0.9995831,0.0001244542,0.00002654239,0.0001419734,0.00005798141,0.00006599568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004841349,0.00005066966,0.0004359571,0.001005778,0.0000927664,0.00001829069,0.0003393538,0.06813859,0.8496696,0.01634023,0.002512778,0.06134752],"study_design_scores_gemma":[0.0008427321,0.0000327403,0.00001904287,0.000152352,0.00001791715,0.00003130057,0.0003457719,0.08615535,0.8686359,0.001804991,0.04152786,0.0004340271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4898579,0.001315685,0.5026613,0.00003022653,0.001310354,0.0003254032,0.000006851321,0.0007873007,0.003704934],"genre_scores_gemma":[0.9432913,0.00003679394,0.05579273,0.00006018026,0.0001762562,0.000005949468,0.00000680554,0.00004817946,0.000581802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4534334,"threshold_uncertainty_score":0.6190435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08155183605874393,"score_gpt":0.285336116808571,"score_spread":0.2037842807498271,"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."}}