{"id":"W3194448140","doi":"10.32920/ryerson.14649366.v1","title":"Multi-objective Tabu search based topology synthesis for designing power and performance efficient NoC architectures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Tabu search; Network on a chip; Multiprocessing; Network topology; Deadlock; Power (physics); Distributed computing; Topology (electrical circuits); Throughput; Parallel computing; Computer engineering; Computer architecture; Embedded system; Engineering; Algorithm; Computer network","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":[0.000912298,0.0002897186,0.000449208,0.0003039778,0.0002546975,0.000372124,0.0006196339,0.0002361878,0.00003434626],"category_scores_gemma":[0.0001651503,0.0002449675,0.0001589998,0.0001861607,0.00005560526,0.0000318155,0.0008868846,0.0004000589,0.000004980751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000487,"about_ca_system_score_gemma":0.0002134544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001593374,"about_ca_topic_score_gemma":0.00007585781,"domain_scores_codex":[0.9976986,0.000275504,0.0003313804,0.001001882,0.0002248507,0.0004677698],"domain_scores_gemma":[0.9979162,0.0009045521,0.00009060447,0.0006186497,0.0003269836,0.0001430204],"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":[0.0001209818,0.0001703673,0.0008995305,0.0004619864,0.0001685812,0.00001746033,0.008621813,0.9656292,0.001868775,0.001048061,0.0002626156,0.02073063],"study_design_scores_gemma":[0.0002028264,0.0001728299,0.001834835,0.0002753646,0.000009300315,0.00002193462,0.0003305252,0.9796089,0.01713741,0.00001219541,0.00007447408,0.000319389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2253716,0.00009362705,0.7718841,0.0003678942,0.001118448,0.0007447829,0.000005087342,0.00009487753,0.0003195675],"genre_scores_gemma":[0.7849541,0.000002209092,0.2140854,0.0003643562,0.00008089228,0.0003332743,0.000001770036,0.00001648723,0.0001615615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5595825,"threshold_uncertainty_score":0.9989483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02736512991449894,"score_gpt":0.2689134646960383,"score_spread":0.2415483347815393,"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."}}