{"id":"W2511078284","doi":"10.1109/iscas.2016.7539121","title":"Generating voltage drop aware current budgets for RC power grids","year":2016,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Intel Corporation","keywords":"Computer science; Voltage drop; Grid; Power network design; Key (lock); Voltage; Process (computing); Task (project management); Metric (unit); Chip; Power (physics); Distributed computing; Computer engineering; Electronic engineering; Reliability engineering; Electrical engineering; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001602985,0.0002390088,0.0001949876,0.00009607441,0.00009032467,0.00004522774,0.0002011639,0.00008086001,0.0004107251],"category_scores_gemma":[0.00003102978,0.0001591688,0.0000877276,0.0001085423,0.00002400691,0.0003314335,0.00003913177,0.00009252885,0.0003550106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163993,"about_ca_system_score_gemma":0.00002430359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002373174,"about_ca_topic_score_gemma":0.000006351279,"domain_scores_codex":[0.9987952,0.000007932653,0.0002849001,0.000248413,0.0001761975,0.0004873812],"domain_scores_gemma":[0.9993872,0.00007666202,0.00002711487,0.0003197706,0.00007499745,0.0001142801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002470784,0.00009421742,0.002848624,0.0003874516,0.0001488709,0.00001030439,0.0006238591,0.004794061,0.5129821,0.002827071,0.2772659,0.1979928],"study_design_scores_gemma":[0.003017197,0.0002939951,0.001105217,0.0003172234,0.00004904314,0.00001354157,0.00007685997,0.2514251,0.2634492,0.0003055005,0.4782887,0.001658416],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1561561,0.000592017,0.8358158,0.0001051215,0.003160658,0.0004553133,0.00004437647,0.0009950662,0.00267554],"genre_scores_gemma":[0.9942608,0.0000844372,0.0032363,0.00006459261,0.0004472571,0.0001135995,0.000008755112,0.00008671173,0.001697478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8381048,"threshold_uncertainty_score":0.6490715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085675052308535,"score_gpt":0.2257947082967586,"score_spread":0.2149379577736732,"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."}}