{"id":"W3107133478","doi":"10.1145/3380446.3430618","title":"An Adaptive Analytic FPGA Placement Framework based on Deep-Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Field-programmable gate array; Encoder; Quality (philosophy); Placer mining; Deep learning; Parallel computing; Computer engineering; Artificial intelligence; Embedded system; 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":[0.00007454774,0.0001385659,0.0001347818,0.00005567282,0.00003962133,0.00003315274,0.0001212192,0.00008878553,0.0007954095],"category_scores_gemma":[0.00002846591,0.0001298799,0.00004410832,0.0001826037,0.000009694967,0.00006574491,0.000008614154,0.0003096663,0.0001034682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004064346,"about_ca_system_score_gemma":0.000006475113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002932754,"about_ca_topic_score_gemma":0.000001076253,"domain_scores_codex":[0.999343,0.00003122612,0.0001228912,0.000173868,0.0001468415,0.0001821596],"domain_scores_gemma":[0.9996085,0.00007013154,0.00001397205,0.000149422,0.0000159755,0.0001420535],"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":[0.00006318407,0.00004110695,0.0003489239,0.00002784638,0.00005364222,0.00002143682,0.0004325413,0.9682662,0.001843506,0.001957721,0.001803285,0.02514061],"study_design_scores_gemma":[0.0001023313,0.0006923434,0.0001249306,0.0000242576,0.00001563946,1.936987e-7,0.0001342919,0.9847881,0.0128506,0.0001519539,0.0009352227,0.0001801359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001855209,0.00004109242,0.9757195,0.0001132744,0.00003120607,0.000140504,0.000001166802,0.001755123,0.02034294],"genre_scores_gemma":[0.9682403,0.000007299369,0.03072826,0.0008621216,0.00008177381,0.00001769842,0.000006057965,0.00003369446,0.00002284845],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.966385,"threshold_uncertainty_score":0.8709176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670366106880872,"score_gpt":0.232767445339965,"score_spread":0.2160637842711562,"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."}}