{"id":"W2037455559","doi":"10.1109/tcad.2011.2165715","title":"Accelerating FPGA Routing Through Parallelization and Engineering Enhancements Special Section on PAR-CAD 2010","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speedup; Computer science; Parallel computing; Field-programmable gate array; Router; Routing (electronic design automation); Overhead (engineering); Heuristic; Gate array; Heuristics; Embedded system; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002565215,0.0003211138,0.0003819336,0.0002271255,0.0001552968,0.000102089,0.0001040432,0.0002200236,0.00001923272],"category_scores_gemma":[0.000004504615,0.0003051916,0.00005411555,0.00025065,0.00003018898,0.0003109198,0.00000134397,0.00034266,0.000003201024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000834629,"about_ca_system_score_gemma":0.0000180453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001373096,"about_ca_topic_score_gemma":0.000004540919,"domain_scores_codex":[0.9985722,0.00008959608,0.0005689733,0.0003173848,0.0001863547,0.0002655156],"domain_scores_gemma":[0.9994249,0.0001019213,0.0001116396,0.0001817357,0.0001052215,0.00007463248],"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":[0.0001087866,0.0003436854,0.00007551198,0.0006133493,0.0005963318,0.0000240699,0.00657185,0.5783478,0.2442624,0.001180094,0.001136345,0.1667397],"study_design_scores_gemma":[0.000671005,0.000872333,0.0002022113,0.0009509692,0.00005673061,0.00004989713,0.0001948365,0.7516632,0.2446836,0.00005535658,0.00009521769,0.0005046567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03854806,0.00008922076,0.9573806,0.000001786827,0.002523899,0.0005484068,0.00001497536,0.0004094559,0.0004836211],"genre_scores_gemma":[0.9947086,0.0002337713,0.00448543,0.0000130493,0.0004060541,0.000068011,0.000005003896,0.00004932129,0.0000307895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9561605,"threshold_uncertainty_score":0.99994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05512664828856144,"score_gpt":0.2195949649687447,"score_spread":0.1644683166801832,"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."}}