{"id":"W1993745690","doi":"10.1109/sbac-padw.2014.16","title":"Efficient Virtual Channel Organization and Congestion Avoidance in Multicore NoC Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Virtual channel; Router; Computer science; Network on a chip; Latency (audio); Embedded system; Flow control (data); Queue; Throughput; Queueing theory; Channel (broadcasting); Multi-core processor; Port (circuit theory); Logic synthesis; Computer network; Computer architecture; Logic gate; Parallel computing; Engineering; Operating system; Electronic engineering","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":[0.0004094609,0.00008267419,0.0001212392,0.00008358964,0.00007127565,0.0001484061,0.0001466658,0.00006062303,0.000002489624],"category_scores_gemma":[0.00008530347,0.00006994661,0.000009189494,0.0002875802,0.00001581744,0.0000969945,0.00006247615,0.00006583682,0.00002328597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003587594,"about_ca_system_score_gemma":0.000009047789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008592088,"about_ca_topic_score_gemma":0.00005326018,"domain_scores_codex":[0.9991726,0.0001092493,0.0002042712,0.0002537603,0.0001200216,0.0001400969],"domain_scores_gemma":[0.9995115,0.00008953461,0.00006053007,0.0001796094,0.0001129033,0.00004594311],"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":[0.000004513252,0.00006350668,0.002417479,0.00003551645,0.000005648309,0.000003116809,0.002082203,0.5232148,0.0006703976,0.4678799,0.0003405646,0.003282402],"study_design_scores_gemma":[0.0002694764,0.00005518217,0.002690086,0.00005011171,7.449432e-7,0.00002572207,0.0001120306,0.996383,0.00008329836,0.00002947982,0.0002068744,0.00009396456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2220799,0.00003478969,0.7759048,0.0001216482,0.001117324,0.0001381021,2.095436e-7,0.0000890044,0.0005141401],"genre_scores_gemma":[0.9991956,0.000003333613,0.000225814,0.00008595335,0.0001169556,0.000008174009,0.000001574654,0.000005938969,0.0003566537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7771156,"threshold_uncertainty_score":0.285234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006819217082897281,"score_gpt":0.1900635450422833,"score_spread":0.183244327959386,"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."}}