{"id":"W4200335229","doi":"10.32920/17303846","title":"Investigating The Efficiency Of The VPR And COFFE Area Models In Predicting The Layout Area Of FPGA Lookup Tables","year":2021,"lang":"en","type":"preprint","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; McGill University","funders":"","keywords":"Routing (electronic design automation); Field-programmable gate array; Lookup table; Computer science; Block (permutation group theory); Benchmark (surveying); Embedded system; Mathematics; Geometry; Geography","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":[0.0008250381,0.0002628702,0.0004020492,0.00007059677,0.0000838779,0.0000642353,0.0006491684,0.0002137439,0.00001404789],"category_scores_gemma":[0.0001920464,0.000133647,0.000108054,0.0002705024,0.0002521593,0.00007347526,0.0007231305,0.0007730087,7.245814e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003538943,"about_ca_system_score_gemma":0.00009066891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005685008,"about_ca_topic_score_gemma":0.000189187,"domain_scores_codex":[0.9985233,0.0001230539,0.0005756341,0.0002515043,0.0002926113,0.0002339267],"domain_scores_gemma":[0.9985949,0.0003856677,0.0001861394,0.0007189682,0.00008288991,0.00003144424],"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":[0.000002816264,0.00005423437,0.02034072,0.001658632,0.0001477899,0.000002373245,0.01700877,0.9346017,0.02061191,0.001539342,0.000501306,0.003530437],"study_design_scores_gemma":[0.0001073738,0.00001365726,0.001409422,0.00173175,0.00005429162,0.000006463872,0.002474699,0.91234,0.0764496,0.005205935,0.000007893913,0.0001988703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361254,0.003531043,0.04816467,0.0001546295,0.0001683928,0.0008670011,0.00004524666,0.0002020588,0.01074156],"genre_scores_gemma":[0.9981586,0.0002978461,0.001313344,0.00004503545,0.00002626141,0.00006994142,0.000006718091,0.00003263696,0.00004963223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06203318,"threshold_uncertainty_score":0.5449967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961020837143393,"score_gpt":0.2135577993138362,"score_spread":0.1839475909424023,"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."}}