{"id":"W2752793715","doi":"10.1109/tcad.2017.2748027","title":"Automatic Selection of Process Corner Simulations for Faster Design Verification","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bottleneck; Benchmark (surveying); Process (computing); Set (abstract data type); Computer science; Transistor; Algorithm; Power (physics); Selection (genetic algorithm); Voltage; Process corners; Speedup; Die (integrated circuit); Integrated circuit; Engineering; Parallel computing; Electrical engineering; Artificial intelligence; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237465,0.001717046,0.001298521,0.001736113,0.0005411459,0.0009494383,0.001056031,0.0007361763,0.005228634],"category_scores_gemma":[0.007301488,0.0007093124,0.0008011551,0.0009429825,0.0003200361,0.001294407,0.0008544563,0.0008895488,0.001654935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004721948,"about_ca_system_score_gemma":0.001403867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001504001,"about_ca_topic_score_gemma":0.002304249,"domain_scores_codex":[0.9985808,0.0005177597,0.0001230104,0.0002492335,0.0004048282,0.0001243365],"domain_scores_gemma":[0.9953002,0.00285601,0.0003520699,0.0006870687,0.0007121617,0.00009258795],"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":[0.001329896,0.0003667882,0.01038924,0.0003691821,0.0001828268,0.0003490956,0.0002856966,0.2669978,0.1539145,0.005834292,0.006697865,0.5532829],"study_design_scores_gemma":[0.0001270973,0.0001079577,0.0009944803,0.00002197119,0.00003369816,0.0001044902,0.00003264581,0.9390118,0.05446206,0.002823355,0.002250454,0.00003007719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07740971,0.0002617388,0.9062983,0.00009896217,0.00003593588,0.0001133459,0.0002036011,0.01410515,0.001473206],"genre_scores_gemma":[0.4437561,0.000103236,0.5526194,0.0000852601,0.00001965011,0.0002089254,0.001006517,0.001437211,0.0007637118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005228634,"threshold_uncertainty_score":0.01749152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05011577823575271,"score_gpt":0.2648827685851671,"score_spread":0.2147669903494144,"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."}}