{"id":"W1991047274","doi":"10.1109/async.2012.25","title":"High-Throughput Low-Energy Content-Addressable Memory Based on Self-Timed Overlapped Search Mechanism","year":2012,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Japan Society for the Promotion of Science; University of Tokyo","keywords":"Computer science; Overhead (engineering); Throughput; Word (group theory); Content-addressable memory; Energy (signal processing); Dissipation; Computer hardware; CMOS; Content-addressable storage; Embedded system; Electronic engineering; Artificial intelligence; Engineering; Operating system","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":[0.00009583565,0.0001545557,0.0002417469,0.000350656,0.00020415,0.0003201023,0.001149044,0.0002155273,0.001571164],"category_scores_gemma":[0.0002709722,0.0001150999,0.000125219,0.0003420247,0.0001677149,0.0007356486,0.0002511947,0.0001471965,0.0002642971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002581385,"about_ca_system_score_gemma":0.0002893989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003158937,"about_ca_topic_score_gemma":0.0008384782,"domain_scores_codex":[0.9999145,0.000009729094,0.000006995985,0.00001675486,0.00003538571,0.00001660038],"domain_scores_gemma":[0.9998206,0.00004285771,0.00004448459,0.00002769174,0.00005171244,0.00001269055],"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":[0.0006377953,0.0001374017,0.00198009,0.0003382521,0.000056184,0.0005854541,0.0001770865,0.008666982,0.8428141,0.01572288,0.003567248,0.1253165],"study_design_scores_gemma":[0.0001995541,0.00107007,0.001810358,0.0000331676,0.0001415581,0.001671603,0.00006669337,0.2010223,0.7727764,0.003702807,0.01744804,0.00005733516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7145893,0.002260095,0.265444,0.0002145435,0.0001730357,0.0001103801,0.0001976894,0.00339286,0.01361815],"genre_scores_gemma":[0.9625995,0.0001588089,0.03468477,0.00006398879,0.00002576279,0.0000428166,0.00007938041,0.0000303977,0.002314572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001571164,"threshold_uncertainty_score":0.005256116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02487416087665568,"score_gpt":0.2306194334969701,"score_spread":0.2057452726203144,"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."}}