{"id":"W2973166705","doi":"10.1109/tpds.2019.2940190","title":"Achieving Flexible Global Reconfiguration in NoCs Using Reconfigurable Rings","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Pearl River S and T Nova Program of Guangzhou; Ministry of Science and Technology of the People's Republic of China; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Control reconfiguration; Computer science; Latency (audio); Embedded system; Overhead (engineering); Interconnection; Computer architecture; Flexibility (engineering); Network packet; Field-programmable gate array; Network on a chip; Distributed computing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000247606,0.0004249567,0.0003292478,0.0003825969,0.0003467291,0.0003535926,0.000518097,0.0002517593,0.000712971],"category_scores_gemma":[0.0004126848,0.000184187,0.0003240013,0.0003736713,0.0003423133,0.0006993219,0.0006075841,0.0002908264,0.0002229937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002459278,"about_ca_system_score_gemma":0.0002200765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005576674,"about_ca_topic_score_gemma":0.001234565,"domain_scores_codex":[0.999783,0.00004684949,0.00001714617,0.00005451163,0.00004015351,0.00005830074],"domain_scores_gemma":[0.9996701,0.00007765727,0.00006930073,0.000115816,0.00003946608,0.00002757705],"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":[0.0003310851,0.00009749029,0.002348443,0.000230162,0.0001080551,0.0005152195,0.0001694881,0.3546347,0.4341924,0.01354281,0.002066287,0.1917637],"study_design_scores_gemma":[0.00005115187,0.0005401645,0.002467332,0.00002782198,0.00008589174,0.0004749954,0.00009911548,0.8431565,0.1358469,0.007040866,0.01014724,0.00006207803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4025664,0.001344266,0.5837582,0.0001415382,0.00008690996,0.00007006907,0.0001045589,0.002701744,0.009226214],"genre_scores_gemma":[0.9083712,0.0002942558,0.08957528,0.00005209669,0.00002311576,0.00005395415,0.0000849597,0.00006574111,0.001479406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000712971,"threshold_uncertainty_score":0.00238508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214933963987428,"score_gpt":0.2471534726324857,"score_spread":0.2250041329926114,"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."}}