{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304989,0.0009261139,0.0008052341,0.0008286522,0.0004798506,0.0009223748,0.001442678,0.0008557881,0.004446387],"category_scores_gemma":[0.00402299,0.000482881,0.0004441483,0.0006496735,0.0005623852,0.001140976,0.0007267399,0.001367115,0.0009812126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075673,"about_ca_system_score_gemma":0.001650906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003694906,"about_ca_topic_score_gemma":0.007051478,"domain_scores_codex":[0.9992931,0.0002200568,0.00003847653,0.0001743463,0.0001782277,0.00009578183],"domain_scores_gemma":[0.9978406,0.001447198,0.0002072202,0.0001738775,0.0002772773,0.00005379954],"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":[0.000182157,0.0001773158,0.001296547,0.00008324371,0.00003844284,0.00005668949,0.00003847182,0.6346683,0.003777319,0.003113815,0.003850011,0.3527178],"study_design_scores_gemma":[0.00001145516,0.00002552515,0.0000910754,0.000005206796,0.000003754756,0.000009951916,0.000006751223,0.9963766,0.001098149,0.002010427,0.0003572737,0.000003874062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03294048,0.0003381552,0.9583317,0.0004244157,0.00007437813,0.0001256957,0.0001041808,0.002967952,0.004693029],"genre_scores_gemma":[0.6385511,0.0002395249,0.3575092,0.0003271475,0.0001119207,0.0001718978,0.0002793716,0.0001920441,0.002617968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004446387,"threshold_uncertainty_score":0.01487464,"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."}}