{"id":"W2925797319","doi":"10.1145/3317575","title":"An Optimized Cost Flow Algorithm to Spread Cells in Detailed Placement","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Placement; Algorithm; Very-large-scale integration; Design flow; Electronic circuit; Physical design; Circuit design; Embedded system; Electrical engineering","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":[0.000417273,0.0008513583,0.0006564214,0.001173578,0.0005206242,0.0007265478,0.0008384846,0.0008346837,0.005467444],"category_scores_gemma":[0.001370552,0.0004343168,0.0004529503,0.001002414,0.0004417643,0.000856486,0.0007104747,0.0006617637,0.001117591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009137601,"about_ca_system_score_gemma":0.001463347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003574265,"about_ca_topic_score_gemma":0.006026529,"domain_scores_codex":[0.9997286,0.00004245403,0.00001576293,0.00004497946,0.000124496,0.00004361076],"domain_scores_gemma":[0.9996013,0.0001900502,0.00004146194,0.00004019211,0.0001079593,0.00001909907],"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":[0.0001783758,0.00009903027,0.0006109332,0.0001059401,0.00002731891,0.00009496867,0.0000803096,0.5619276,0.0202286,0.01647323,0.004347458,0.3958263],"study_design_scores_gemma":[0.00002949922,0.00007536204,0.00009538882,0.000007020652,0.00000717832,0.00004788411,0.00001298402,0.989005,0.003333513,0.005142946,0.00223673,0.000006568164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008767131,0.00008009924,0.9889636,0.00006995194,0.00002206566,0.00008949225,0.00004451422,0.0005080313,0.001455084],"genre_scores_gemma":[0.0919805,0.00009399479,0.9042928,0.00006695817,0.00001926026,0.0001810422,0.0002003737,0.0001383876,0.003026717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005467444,"threshold_uncertainty_score":0.0182904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082779726984855,"score_gpt":0.2334949140387263,"score_spread":0.2226671167688778,"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."}}