{"id":"W4387123818","doi":"10.1109/sbcci60457.2023.10261650","title":"FPGA Placement: Dynamic Decision Making Via Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Netlist; Field-programmable gate array; Computer science; Placement; Set (abstract data type); Design flow; Multi-core processor; Parallel computing; Central processing unit; Embedded system; Computer architecture; Computer engineering; Physical design; Computer hardware; Circuit design","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":[0.0001794657,0.0001180333,0.0001075788,0.0001876903,0.00006875151,0.00003205461,0.0001114347,0.00006399718,0.0003533443],"category_scores_gemma":[0.00001755561,0.0001102158,0.00004069656,0.0003120754,0.000007083555,0.00008206515,0.00005070778,0.0001710532,0.0005101715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004508334,"about_ca_system_score_gemma":0.000002395026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004482732,"about_ca_topic_score_gemma":0.00001433918,"domain_scores_codex":[0.9993377,0.00001156054,0.0001532527,0.0001282797,0.0001364779,0.0002327406],"domain_scores_gemma":[0.9997316,0.00007945555,0.00001259167,0.0001335043,0.00001035924,0.00003250823],"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":[0.00001570827,0.00001262477,0.0009086154,0.00006376539,0.00005870249,0.00005797405,0.0002627082,0.09855361,0.04235755,0.0002170659,0.006613375,0.8508783],"study_design_scores_gemma":[0.0001251455,0.000034187,0.0005374764,0.00005343078,0.000007622499,0.000007555278,0.00002820347,0.9862588,0.006565627,0.001298212,0.004894988,0.0001887718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02998677,0.0002766467,0.9482157,0.00001629276,0.0001768161,0.0001385124,0.000002261139,0.006169664,0.01501737],"genre_scores_gemma":[0.9916684,0.000160845,0.007364748,0.00002301471,0.00002027916,0.00001633058,0.0000170984,0.00004534502,0.000683938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9616816,"threshold_uncertainty_score":0.6557393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00851885835249342,"score_gpt":0.2487240653706987,"score_spread":0.2402052070182053,"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."}}