{"id":"W2083551465","doi":"10.1109/wamca.2012.11","title":"Optimal Virtual Channel Insertion for Contention Alleviation and Deadlock Avoidance in Custom NoCs","year":2012,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deadlock; Computer science; Benchmark (surveying); Virtual channel; Deadlock prevention algorithms; Network on a chip; Performance improvement; Channel (broadcasting); Power (physics); Resource (disambiguation); Embedded system; Distributed computing; Parallel computing; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005119933,0.0002784976,0.0002869471,0.0005724737,0.0003472801,0.0004192415,0.000838465,0.0001913079,0.0006700468],"category_scores_gemma":[0.001232677,0.0002334691,0.0001757375,0.0004336181,0.0004018333,0.0006820269,0.0005889916,0.0002713825,0.00008005731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000424158,"about_ca_system_score_gemma":0.0008830749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009095065,"about_ca_topic_score_gemma":0.00234669,"domain_scores_codex":[0.9996185,0.0001018255,0.000033147,0.00004564577,0.0001066199,0.00009420252],"domain_scores_gemma":[0.9990145,0.0003411736,0.0002238639,0.0001996226,0.0001559422,0.00006488731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008415757,0.0004523259,0.005609183,0.0002671644,0.00005741308,0.0003627262,0.0002934193,0.1966267,0.4623336,0.01003648,0.001323108,0.3217962],"study_design_scores_gemma":[0.00007248198,0.0004646921,0.001722219,0.00002463659,0.00005808609,0.0003376295,0.00008055884,0.7685763,0.2229177,0.002882271,0.002812684,0.00005071971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056999,0.0006163023,0.4896356,0.00008973959,0.00005839606,0.00007510928,0.00002999563,0.001417153,0.002377821],"genre_scores_gemma":[0.9203274,0.00009174957,0.07878973,0.00003014816,0.00001130235,0.00002108114,0.00002805544,0.00004576932,0.0006548163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009095065,"threshold_uncertainty_score":0.003077507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02362048649212971,"score_gpt":0.2442883072106944,"score_spread":0.2206678207185646,"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."}}