{"id":"W2175591235","doi":"10.1109/hpcc-css-icess.2015.313","title":"A Framework for Learning Based DVFS Technique Selection and Frequency Scaling for Multi-core Real-Time Systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Frequency scaling; Computer science; Energy consumption; Reinforcement learning; Multi-core processor; Scheduling (production processes); Throughput; Scaling; Embedded system; Distributed computing; Real-time computing; Parallel computing; Artificial intelligence; Operating system; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868819,0.0001318545,0.000184407,0.0000737882,0.00009326454,0.00004662466,0.00004659584,0.0002022566,0.000004199232],"category_scores_gemma":[0.0004549155,0.0001259197,0.00004745718,0.0001238516,0.00001361247,0.00008870747,0.000007687996,0.0001458612,0.000001329382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674754,"about_ca_system_score_gemma":0.00004301838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000229122,"about_ca_topic_score_gemma":0.00002106146,"domain_scores_codex":[0.9992807,0.00002143674,0.0001908278,0.0001843777,0.00006908726,0.0002535701],"domain_scores_gemma":[0.9993804,0.0001766138,0.00002757758,0.0001038601,0.0002132192,0.00009840012],"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":[0.0004547281,0.0003058326,0.1088424,0.01176141,0.0002913769,0.00000818466,0.003637857,0.7099874,0.1029326,0.0442284,0.003017623,0.0145322],"study_design_scores_gemma":[0.0004299167,0.0001158518,0.000239695,0.00006178171,0.00001550259,0.000002754423,0.0003506064,0.9932862,0.001451515,0.003518185,0.0003502598,0.0001777518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0787316,0.00006125925,0.918835,0.00001678613,0.00009074681,0.00133805,0.000004113995,0.0007037791,0.0002186828],"genre_scores_gemma":[0.7020144,0.000001378061,0.2971361,0.000003724764,0.00005856713,0.0005802712,0.00001014815,0.00003412968,0.0001612615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6232828,"threshold_uncertainty_score":0.5134856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04146231027471969,"score_gpt":0.2891842095720009,"score_spread":0.2477218992972812,"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."}}