{"id":"W3011140373","doi":"10.1109/icm48031.2019.9021943","title":"Enhancing the Performance of FPGA Congestion Management via Supervised Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Field-programmable gate array; Computer science; Lookup table; Key (lock); Computer architecture; Embedded system; Placement; Computer engineering; Physical design; Circuit design; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001637694,0.00006436493,0.00007679088,0.00003762529,0.0000234022,0.00001014985,0.00009042241,0.00002909932,0.0001811332],"category_scores_gemma":[0.000001298299,0.00004614827,0.00002287588,0.00007569569,0.000008598418,0.00007819787,0.00001930236,0.00009123366,0.00007314179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001353631,"about_ca_system_score_gemma":0.000001398219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004996175,"about_ca_topic_score_gemma":0.000001010337,"domain_scores_codex":[0.9996192,0.00001114812,0.0001211713,0.00006182404,0.00008350226,0.0001031406],"domain_scores_gemma":[0.9998093,0.00002133432,0.00001330619,0.0001297751,0.00001477726,0.00001156398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001586648,0.00002193978,0.009757165,0.0008520962,0.0001415172,0.000001796603,0.0008440226,0.06146582,0.6948844,0.001925092,0.0003992917,0.2296909],"study_design_scores_gemma":[0.000165888,0.00009619634,0.005769767,0.00008290271,0.00001853766,0.000002351835,0.0001900148,0.2165617,0.7753682,0.00003241461,0.001571244,0.0001407664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9323347,0.00009207069,0.03805535,0.00001103809,0.00008416706,0.0002109314,1.165981e-7,0.0003652613,0.02884636],"genre_scores_gemma":[0.9976345,0.0001233698,0.001456383,0.00001440219,0.00001240837,0.00001195321,0.000001471044,0.0000124497,0.0007330563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2295502,"threshold_uncertainty_score":0.1983281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004616403771878333,"score_gpt":0.1783628824445926,"score_spread":0.1737464786727143,"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."}}