{"id":"W4280560843","doi":"10.23919/date54114.2022.9774530","title":"CR&amp;P: An Efficient Co-operation between Routing and Placement","year":2022,"lang":"en","type":"article","venue":"2022 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Routing (electronic design automation); Physical design; Electronic design automation; Placement; Design flow; CONTEST; Network routing; Place and route; Integer programming; Distributed computing; Embedded system; Circuit design; Algorithm","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":[0.001442654,0.001031224,0.0008021218,0.001148888,0.0007108322,0.001587143,0.002745011,0.0008747273,0.005985034],"category_scores_gemma":[0.003058356,0.0005334332,0.0007573716,0.001037418,0.001001105,0.001710112,0.002288315,0.001376789,0.001789531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008866899,"about_ca_system_score_gemma":0.003181886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00311066,"about_ca_topic_score_gemma":0.004977939,"domain_scores_codex":[0.998252,0.0003644167,0.00008689056,0.0003120068,0.0008053011,0.0001793838],"domain_scores_gemma":[0.9985726,0.0003827023,0.000185247,0.0005477424,0.0002424062,0.00006931714],"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":[0.0002369545,0.0002387785,0.00120656,0.0003295337,0.00007379625,0.000441769,0.000188978,0.2751642,0.02801561,0.06603171,0.01549021,0.6125818],"study_design_scores_gemma":[0.00005162816,0.0001948129,0.0001932753,0.00002581934,0.00002920262,0.0003275256,0.00003982726,0.9571465,0.01494476,0.01357036,0.01345208,0.00002416578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007069266,0.0001426203,0.9847403,0.000121873,0.00004601935,0.0001232858,0.00004799208,0.003153748,0.004554866],"genre_scores_gemma":[0.153554,0.0001358291,0.8418561,0.0001113656,0.00003870866,0.0001492726,0.0001753862,0.0004478608,0.003531387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005985034,"threshold_uncertainty_score":0.02002192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0573484131648651,"score_gpt":0.2814321515290119,"score_spread":0.2240837383641468,"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."}}