{"id":"W3107243966","doi":"10.1109/ccece47787.2020.9255748","title":"Learning-Based Reconfigurable Cache for Heterogeneous Chip Multiprocessors","year":2020,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Control reconfiguration; Parallel computing; Cache algorithms; Latency (audio); Energy consumption; Computer architecture; Embedded system; Cache pollution; Multiprocessing; Smart Cache; Chip; CPU cache","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":[0.000143119,0.0001128391,0.0001330665,0.00003978824,0.0001511783,0.0001206291,0.0005452838,0.0000543789,0.00002168795],"category_scores_gemma":[0.0001698496,0.0001033246,0.00007208944,0.0002157568,0.00001402896,0.0001081362,0.00003494876,0.00009847153,0.00002562587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001222191,"about_ca_system_score_gemma":0.00005494997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008520828,"about_ca_topic_score_gemma":0.000001059075,"domain_scores_codex":[0.9991216,0.00004085142,0.0001690856,0.000351664,0.0001033268,0.0002134604],"domain_scores_gemma":[0.9994312,0.0001063562,0.00007486602,0.0001718601,0.0001013447,0.0001143941],"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":[0.00001735394,0.00003377119,0.0001667748,0.00004187761,0.0000108941,0.000002653296,0.0004126873,0.9791248,0.0003517848,0.0008519742,0.003443614,0.01554181],"study_design_scores_gemma":[0.0002754146,0.0002319893,0.000006486865,0.000005293629,0.000001854023,0.000001539379,0.000004623451,0.931002,0.05732731,0.000164829,0.01083758,0.0001410551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001204974,0.00004150298,0.9915437,0.002980034,0.00004443391,0.0002010733,7.016989e-7,0.001506445,0.002477125],"genre_scores_gemma":[0.7118095,0.000003061004,0.2850845,0.002629863,0.00003559078,0.00002848803,0.000005041157,0.00001044485,0.0003934884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7106045,"threshold_uncertainty_score":0.4213455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03210648535291662,"score_gpt":0.2587785205998593,"score_spread":0.2266720352469427,"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."}}