{"id":"W2123282679","doi":"10.1109/tvlsi.2010.2047123","title":"IR-Drop Aware Clustering Technique for Robust Power Grid in FPGAs","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Power network design; Cluster analysis; Field-programmable gate array; Computer science; Voltage drop; Drop (telecommunication); Grid; Very-large-scale integration; Electronic circuit; Chip; Voltage; Embedded system; Electronic engineering; Parallel computing; Engineering; Electrical engineering; Artificial intelligence; Mathematics; Telecommunications","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":[0.0002180505,0.0004211955,0.0004197481,0.0006181261,0.0006380207,0.0004549536,0.001054309,0.0003839581,0.001708621],"category_scores_gemma":[0.000780485,0.0002203636,0.0002552599,0.0006146345,0.0002301858,0.0006515458,0.0004028301,0.0003970469,0.0004982005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007905771,"about_ca_system_score_gemma":0.000386151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551776,"about_ca_topic_score_gemma":0.003447142,"domain_scores_codex":[0.9997188,0.00002920834,0.00001676773,0.00006384935,0.0001324301,0.00003887564],"domain_scores_gemma":[0.9996169,0.00007017678,0.00006541931,0.00009239663,0.0001348073,0.00002032532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006386081,0.0001900073,0.002072244,0.0001909502,0.00007960461,0.0003344144,0.000214821,0.1967441,0.3079185,0.006201644,0.006390825,0.4790243],"study_design_scores_gemma":[0.00007929857,0.0004378859,0.002617403,0.00001982477,0.00005718365,0.0006168271,0.0001023596,0.7531432,0.231985,0.002948245,0.007952702,0.00004014396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063533,0.0004019596,0.885269,0.000226895,0.00009184163,0.00007436817,0.00006828071,0.002777934,0.004736352],"genre_scores_gemma":[0.8188309,0.000197523,0.1765541,0.000141087,0.00005812684,0.00004588268,0.0001593851,0.0001724733,0.00384057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001708621,"threshold_uncertainty_score":0.005736113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014703397859544,"score_gpt":0.2193356172416236,"score_spread":0.2091885832630281,"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."}}