{"id":"W2039194939","doi":"10.1145/2501985","title":"Analyzing System-Level Information’s Correlation to FPGA Placement","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Netlist; Computer science; Locality; Field-programmable gate array; Placement; Simulated annealing; Computer engineering; Verilog; Algorithm; Theoretical computer science; Embedded system; Physical design; Circuit design","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001733532,0.0001858729,0.0002472303,0.00100273,0.0002000013,0.0001008951,0.000199329,0.0003116875,0.00007892332],"category_scores_gemma":[0.00001610029,0.0001810498,0.00003490331,0.0005323902,0.00002664611,0.0004479007,0.000002701445,0.000274124,0.0005074194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001449483,"about_ca_system_score_gemma":0.00001099551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009883931,"about_ca_topic_score_gemma":0.000007148881,"domain_scores_codex":[0.9990455,0.00002215976,0.0004204889,0.0001577432,0.00009578488,0.0002583615],"domain_scores_gemma":[0.9992952,0.00005592305,0.00005054761,0.0004251216,0.00009019514,0.00008301598],"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":[0.00003917686,0.00008899441,0.0009554978,0.001471718,0.0006192043,0.000006967229,0.00108334,0.2445618,0.01257752,0.01223798,0.01243873,0.7139191],"study_design_scores_gemma":[0.00391374,0.002047106,0.003063471,0.003718764,0.0004241552,0.001117697,0.028029,0.702758,0.1808814,0.003791038,0.06589986,0.004355842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02361107,0.0002675265,0.9660368,0.0003286325,0.0005869186,0.001082367,0.00003369871,0.002321746,0.00573122],"genre_scores_gemma":[0.9969844,0.0000631466,0.001547508,0.00002758006,0.00001281014,0.0008314315,0.000008289365,0.00001817543,0.0005066235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9733734,"threshold_uncertainty_score":0.7382997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392183303958959,"score_gpt":0.2059538184835625,"score_spread":0.1920319854439729,"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."}}