{"id":"W2097546193","doi":"10.1007/s11081-007-9004-4","title":"Modeling leakage power reduction in VLSI as optimization problems","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Subthreshold conduction; Very-large-scale integration; Computer science; CMOS; Leakage (economics); Reduction (mathematics); Power optimization; Electronic engineering; Dissipation; Transistor; Cluster analysis; Sizing; Power (physics); Computer engineering; Electrical engineering; Embedded system; Engineering; Voltage; Mathematics","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":[0.000304158,0.0002001482,0.0001552883,0.0004160379,0.00004872836,0.00006255157,0.00006805609,0.0001384787,0.00005421334],"category_scores_gemma":[0.00002213036,0.000232243,0.00002290189,0.000497463,0.00000988298,0.0005970874,0.00001672624,0.0001869895,0.000008091603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001234113,"about_ca_system_score_gemma":0.00000898627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000011385,"about_ca_topic_score_gemma":0.000002415197,"domain_scores_codex":[0.998979,0.000006133873,0.0003376743,0.0002030942,0.0001435458,0.000330494],"domain_scores_gemma":[0.9996983,0.00001219679,0.0000198678,0.0001368556,0.00004110172,0.00009161838],"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":[0.000004643334,0.000009864494,0.00003530246,0.00005329794,0.000008587819,0.000002802622,0.0005087271,0.9967108,0.002245984,0.0001184337,0.00001122341,0.000290368],"study_design_scores_gemma":[0.0003649801,0.00002041498,0.00004695834,0.00006597063,0.000006022104,0.00002184336,0.00009075891,0.9980228,0.0009935104,0.000004277279,0.00009958137,0.0002628936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0854639,0.0002356255,0.911542,0.00001684086,0.0004284657,0.0002067447,7.111158e-7,0.0004754764,0.001630265],"genre_scores_gemma":[0.9334368,0.0004272083,0.06589938,0.000009874539,0.00006176385,0.00001544784,0.00002434828,0.00007274994,0.00005243256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8479729,"threshold_uncertainty_score":0.9470596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005268158353263225,"score_gpt":0.180044430157686,"score_spread":0.1747762718044227,"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."}}