{"id":"W2560205416","doi":"10.1109/mcsoc.2016.12","title":"Adaptive VC Organization and Arbitration for Efficient NoC Design","year":2016,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Router; Computer science; Network on a chip; Core router; Arbitration; One-armed router; Computer network; Latency (audio); Virtual channel; Network packet; Throughput; Queue; Distributed computing; Embedded system; Queueing theory; Operating system; Wireless","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592976,0.0002573667,0.0002706074,0.0004614319,0.0002426524,0.0005270753,0.001008863,0.0002606751,0.0008887529],"category_scores_gemma":[0.001055342,0.0001692396,0.0001606979,0.0004310337,0.0002317622,0.000586048,0.0003160679,0.0003266095,0.00013223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004912735,"about_ca_system_score_gemma":0.0005246146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008091647,"about_ca_topic_score_gemma":0.001615389,"domain_scores_codex":[0.9996705,0.0000976975,0.0000320271,0.00005432015,0.00009488838,0.00005058069],"domain_scores_gemma":[0.9995452,0.0001332594,0.0000804141,0.0000609085,0.0001533805,0.00002691181],"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":[0.000246691,0.0001368451,0.002292133,0.0002891571,0.00007583553,0.0002996463,0.0001967939,0.3678979,0.2198433,0.05268503,0.003285919,0.3527507],"study_design_scores_gemma":[0.00002314653,0.0001771964,0.0004126955,0.00001854591,0.00003119638,0.0001619919,0.000019383,0.9569756,0.0305583,0.004872383,0.006731985,0.00001757677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04754271,0.001062192,0.9461597,0.0001373576,0.00007295793,0.00009970265,0.00003567635,0.0008824531,0.004007273],"genre_scores_gemma":[0.7044852,0.0003707845,0.2932459,0.00009205189,0.00004057627,0.0001281498,0.00006482288,0.00007016322,0.001502244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001008863,"threshold_uncertainty_score":0.003564477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02383799061690288,"score_gpt":0.2157696117267462,"score_spread":0.1919316211098433,"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."}}