{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005617563,0.0007407791,0.0005766208,0.0004262241,0.0002137428,0.0008504388,0.0009951361,0.001041715,0.002331331],"category_scores_gemma":[0.002129082,0.0005520599,0.0005755484,0.0008156609,0.0006227191,0.0013488,0.0004711607,0.0007395924,0.0003132107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000697316,"about_ca_system_score_gemma":0.0005429778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001817395,"about_ca_topic_score_gemma":0.002447586,"domain_scores_codex":[0.9997409,0.00009769869,0.000009182245,0.00003464666,0.0000837136,0.00003390823],"domain_scores_gemma":[0.9996376,0.0002612919,0.00003615526,0.00002309591,0.00003449068,0.000007467382],"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":[0.00000949821,0.00001222665,0.00007475483,0.00003758385,0.000008918872,0.00001492758,0.00001141924,0.9766937,0.0005928507,0.01598459,0.0002774138,0.006282118],"study_design_scores_gemma":[0.000003542811,0.00000518437,0.0000214896,0.000002954844,0.000004437498,0.000004826672,0.000002792802,0.9916453,0.0003487085,0.00755308,0.0004065397,0.000001222067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04045362,0.001330964,0.9430309,0.0007726703,0.00006417732,0.00005521762,0.00007431087,0.0001891545,0.01402902],"genre_scores_gemma":[0.7692343,0.002083186,0.2098171,0.0003505152,0.0001341894,0.000222738,0.0001302785,0.0002877876,0.01773986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002331331,"threshold_uncertainty_score":0.007799029,"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."}}