{"id":"W4255584984","doi":"10.32920/ryerson.14645025","title":"Congestion aware adaptive routing for network-on-chip communication","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Latency (audio); Header; Network congestion; Network packet; Routing (electronic design automation); Throughput; Network on a chip; Routing domain; Static routing; Routing protocol; Telecommunications; Wireless","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":[],"consensus_categories":[],"category_scores_codex":[0.0009253339,0.0002444171,0.0003514349,0.00006208695,0.0003180703,0.0006290791,0.001014906,0.0003246446,0.00001615715],"category_scores_gemma":[0.00005660679,0.0002325632,0.0002250296,0.0001549426,0.00002188117,0.0001790396,0.001038576,0.0005260973,0.00001101215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001462721,"about_ca_system_score_gemma":0.0001259972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001864823,"about_ca_topic_score_gemma":0.0002746092,"domain_scores_codex":[0.9980579,0.0003463946,0.0004615276,0.0006302692,0.0002013311,0.0003025578],"domain_scores_gemma":[0.9972082,0.0005594328,0.0003605991,0.001319036,0.0004883532,0.00006438402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002661207,0.00005890387,0.0001103347,0.00006631613,0.0001440008,0.000003224838,0.001224991,0.3768353,0.000004046066,0.5846821,0.01628762,0.02055661],"study_design_scores_gemma":[0.0001922195,0.00008461465,0.0001858933,0.0008048432,0.00001145822,0.000008175734,0.0002530453,0.9913602,0.000039615,0.005099567,0.001665238,0.0002950926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00106522,0.0002582695,0.9862135,0.001124467,0.003289076,0.0007137519,0.000004492381,0.00027853,0.007052678],"genre_scores_gemma":[0.9593523,0.00003795143,0.038079,0.0005631933,0.0006902422,0.0002709794,0.0001034105,0.00001929641,0.0008836249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9582871,"threshold_uncertainty_score":0.948365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05430929928577852,"score_gpt":0.2793790470994055,"score_spread":0.225069747813627,"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."}}