{"id":"W2139061105","doi":"10.1109/tcad.2013.2293067","title":"Variation-Aware Geometric Programming Models for the Clock Network Buffer Sizing Problem","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Compute Canada; CMC Microsystems","keywords":"Geometric programming; Skew; Mathematical optimization; Sizing; Computer science; Reduction (mathematics); Robustness (evolution); Heuristic; Clock network; Clock skew; Mathematics; Jitter","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":[0.001459531,0.002069554,0.0009995427,0.0009399609,0.000430069,0.001569628,0.001836273,0.001213933,0.00517911],"category_scores_gemma":[0.003839555,0.0008014659,0.001526118,0.001323247,0.0009447617,0.001811388,0.001056596,0.001949473,0.0007865832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001707863,"about_ca_system_score_gemma":0.00147546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002578373,"about_ca_topic_score_gemma":0.003822556,"domain_scores_codex":[0.9990156,0.0004015376,0.00002914529,0.0001501012,0.0002951711,0.0001083689],"domain_scores_gemma":[0.9987311,0.00084623,0.0001492512,0.00009342797,0.0001443028,0.0000356938],"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":[0.00001457061,0.00001585438,0.00008579131,0.00003309518,0.000008075501,0.00001733468,0.00001475102,0.9739081,0.0003686407,0.01695232,0.0005546234,0.008026956],"study_design_scores_gemma":[0.000004475251,0.00001503552,0.00002121712,0.000005601111,0.00000420217,0.00001245031,0.000006015124,0.9916078,0.000225394,0.007300748,0.0007933833,0.000003793839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005960625,0.0002616604,0.9885557,0.000213223,0.00003719385,0.00005135547,0.00007310956,0.0001290599,0.004718018],"genre_scores_gemma":[0.3205881,0.00142018,0.668327,0.0003449814,0.0001306697,0.0005218532,0.0003937096,0.0004132921,0.007860237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00517911,"threshold_uncertainty_score":0.01732582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0290336320966531,"score_gpt":0.2128726883042761,"score_spread":0.183839056207623,"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."}}