{"id":"W2903751892","doi":"10.1109/norchip.2018.8573462","title":"Multi-Swarm based NoC Configuration and Synthesis","year":2018,"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":"","keywords":"Network on a chip; Computer science; Interconnection; Swarm behaviour; Particle swarm optimization; Latency (audio); Power consumption; Tabu search; Chip; Parallel computing; Power (physics); Embedded system; Computer network; Algorithm; Telecommunications","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":[0.0001837825,0.00005278339,0.00006548007,0.00003827129,0.0000985244,0.0001327149,0.0001285692,0.0000335888,0.00008341448],"category_scores_gemma":[0.00002964881,0.00004088628,0.00001781825,0.00008054971,0.00002854957,0.0001697003,0.00002213434,0.00002457503,0.0001044459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009182151,"about_ca_system_score_gemma":0.0000127323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006637847,"about_ca_topic_score_gemma":0.0001024612,"domain_scores_codex":[0.9995182,0.00004254611,0.0001063695,0.000171179,0.00006838598,0.00009337852],"domain_scores_gemma":[0.9995788,0.0000874766,0.00002999622,0.000180048,0.00008523485,0.00003846843],"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":[0.0000760555,0.0006336841,0.004085344,0.000122697,0.0001481625,0.00003133591,0.004976569,0.0006340896,0.05257678,0.4988484,0.07528912,0.3625778],"study_design_scores_gemma":[0.0001454342,0.00004815689,0.0007457407,0.0000133027,0.000001375235,0.000005664233,0.00002317518,0.9671928,0.0257906,0.00003872691,0.005921518,0.00007356146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007175365,0.00000920823,0.9821932,0.0005536702,0.0004737132,0.00006498337,2.546192e-7,0.00009929134,0.00943026],"genre_scores_gemma":[0.9750766,7.178033e-7,0.02339549,0.0005362514,0.0001294945,0.00000954011,1.835151e-7,0.000002541931,0.0008491796],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9679012,"threshold_uncertainty_score":0.1667294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582180701561709,"score_gpt":0.2483482424896439,"score_spread":0.2225264354740268,"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."}}