{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008606181,0.0001750839,0.0002776358,0.0004996928,0.0004155471,0.0001023582,0.0002657829,0.0002679104,0.000005931669],"category_scores_gemma":[0.0002398315,0.000166947,0.00004426406,0.000350629,0.00006287421,0.0002584378,0.000004841919,0.0003948429,0.000006370183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004549843,"about_ca_system_score_gemma":0.00003401236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004937195,"about_ca_topic_score_gemma":0.000006952363,"domain_scores_codex":[0.9987267,0.0001039949,0.000301198,0.0005340443,0.0001019849,0.0002320366],"domain_scores_gemma":[0.9983649,0.0008433661,0.0001251417,0.000521637,0.00008963289,0.00005534105],"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":[0.000007544786,0.00008395963,0.001826941,0.0001466728,0.00003404829,5.782431e-7,0.00009594772,0.2148036,0.01150522,0.006194105,0.000001596554,0.7652998],"study_design_scores_gemma":[0.0007192838,0.0004453915,0.0003503687,0.0001362261,0.00002035031,0.00005117789,0.00006809836,0.9906301,0.002203065,0.005064239,0.0001574764,0.0001542171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01943288,0.0002214284,0.9773868,0.001157793,0.0003889729,0.0005484886,0.00001915318,0.0005404383,0.0003040454],"genre_scores_gemma":[0.9755232,0.00001572529,0.0239485,0.00007232933,0.000009941386,0.0001434778,0.000008168287,0.00001323794,0.0002654754],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9560903,"threshold_uncertainty_score":0.68079,"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."}}