{"id":"W2967974643","doi":"10.1145/3337930","title":"Novel Congestion-estimation and Routability-prediction Methods based on Machine Learning for Modern FPGAs","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Router; Field-programmable gate array; Overhead (engineering); Machine learning; Routing (electronic design automation); Artificial intelligence; Parallel computing; Embedded system; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006963614,0.001122821,0.0006328006,0.0008608872,0.000338806,0.0006445785,0.001327937,0.0007202798,0.001846862],"category_scores_gemma":[0.002637862,0.0004436287,0.0005048277,0.0006554558,0.0003884383,0.001321446,0.0004878518,0.001213795,0.0004223631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004416,"about_ca_system_score_gemma":0.001100883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00616651,"about_ca_topic_score_gemma":0.008613707,"domain_scores_codex":[0.9996006,0.00008012062,0.00002476179,0.0001152318,0.0001231977,0.00005609523],"domain_scores_gemma":[0.9986248,0.0006326175,0.0002534875,0.0001591396,0.0002827048,0.00004732173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006215496,0.00006798458,0.001987034,0.00003266624,0.0000331191,0.00004205098,0.00002090908,0.8709844,0.002756589,0.001487892,0.0009388069,0.1215864],"study_design_scores_gemma":[0.000001884913,0.00001247887,0.0001218084,0.000001559715,0.00000255897,0.000007504581,0.000001565627,0.9986186,0.0005594081,0.0005648981,0.0001051631,0.000002593049],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02618053,0.00027851,0.9707879,0.0001622765,0.00004197095,0.00003635745,0.00005665082,0.001689702,0.0007660774],"genre_scores_gemma":[0.704444,0.0003102388,0.2911442,0.0001335099,0.00009333736,0.0001129869,0.0002315371,0.0001735066,0.003356726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00616651,"threshold_uncertainty_score":0.01226127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02643159447038476,"score_gpt":0.2736213146217128,"score_spread":0.247189720151328,"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."}}