{"id":"W2013313624","doi":"10.1109/eit.2006.252175","title":"Algorithms for Budget Management with Gate-Sizing and Other Low-Power Applications","year":2006,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Sizing; Power (physics); Power management; Power budget; Low-power electronics; Power optimization; CMOS; Electronic engineering; Dissipation; Engineering; Electrical engineering; Voltage; Switched-mode power supply","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":[0.00007589519,0.0001586193,0.0001168859,0.00008823846,0.00007282539,0.00005384077,0.00009577006,0.00004306056,0.00005342126],"category_scores_gemma":[3.301657e-7,0.000129425,0.00002119712,0.0001511873,0.00003054817,0.0001238073,0.00001685079,0.00005006089,0.00004837955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003625603,"about_ca_system_score_gemma":0.000003555484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001364907,"about_ca_topic_score_gemma":0.00001491793,"domain_scores_codex":[0.9992808,0.000002833806,0.0001507917,0.0001952914,0.0001056096,0.0002646711],"domain_scores_gemma":[0.9996747,0.00002089188,0.00001722516,0.0002219032,0.0000219236,0.00004338197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002336847,0.001010338,0.01553592,0.004618422,0.001852796,0.00005551935,0.001746787,0.3756027,0.01794599,0.3087141,0.1276248,0.145059],"study_design_scores_gemma":[0.003765648,0.0001983146,0.005503054,0.0001279211,0.0001446025,0.00002374483,0.0003881419,0.1816486,0.02136539,0.001937019,0.7834136,0.001484008],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01972478,0.0002676819,0.908896,0.00005408812,0.00008461433,0.001182395,0.00001625617,0.0006282639,0.06914596],"genre_scores_gemma":[0.8621461,0.00003166021,0.1326288,0.0001883308,0.0001396761,0.001080389,0.0000157768,0.0001197049,0.003649499],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8424214,"threshold_uncertainty_score":0.5277799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004766202051268733,"score_gpt":0.1926046765661623,"score_spread":0.1878384745148935,"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."}}