{"id":"W2185362417","doi":"","title":"Low-Power High-Performance Ternary Content Addressable Memory Circuits","year":2006,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Content-addressable memory; Ternary operation; Content (measure theory); Power (physics); Computer science; Artificial intelligence; Mathematics; Programming language; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258968,0.0002028051,0.0002536212,0.0003043256,0.0003110678,0.001080451,0.0008847083,0.0003564254,0.01475095],"category_scores_gemma":[0.0006324232,0.0000835212,0.0001080817,0.0003985682,0.0001713917,0.001190777,0.0004147907,0.0003823457,0.00234487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006420124,"about_ca_system_score_gemma":0.0005184638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004071782,"about_ca_topic_score_gemma":0.002008926,"domain_scores_codex":[0.9998782,0.00001336665,0.000006288869,0.00001583691,0.00005671697,0.00002960311],"domain_scores_gemma":[0.9998154,0.00005933208,0.00002342646,0.00002634261,0.0000637803,0.00001174151],"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":[0.0006064677,0.000192117,0.001067339,0.0008774062,0.00006346808,0.0003297632,0.0003385782,0.008811727,0.3966516,0.1454076,0.05454563,0.3911083],"study_design_scores_gemma":[0.0001651081,0.0006705857,0.001967627,0.0002646973,0.000166194,0.001012847,0.0002254716,0.1854881,0.5707431,0.05508365,0.1841549,0.00005781744],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4218304,0.01189434,0.2293425,0.004202563,0.001523035,0.0002409239,0.001718736,0.005071403,0.3241761],"genre_scores_gemma":[0.9100741,0.00145062,0.03660136,0.0004581327,0.0001771962,0.00005729543,0.0006520329,0.0001642703,0.05036505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01475095,"threshold_uncertainty_score":0.04934686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105706994047953,"score_gpt":0.181300326171254,"score_spread":0.1702432562307744,"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."}}