{"id":"W2844974720","doi":"10.1109/tcad.2018.2855165","title":"ShuttleNoC: Power-Adaptable Communication Infrastructure for Many-Core Processors","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Natural Science Foundation of China; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Control reconfiguration; Computer science; Network on a chip; Latency (audio); Power consumption; Network packet; Bandwidth (computing); Traverse; Airfield traffic pattern; Many core; Provisioning; Power (physics); Electrical efficiency; Distributed computing; Embedded system; Computer network; Telecommunications; Parallel computing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002462148,0.0005818487,0.0003040417,0.0005625265,0.0003646906,0.0005109656,0.001567328,0.0003740815,0.003223982],"category_scores_gemma":[0.0006867638,0.0001689099,0.0002332457,0.0004408669,0.0003181864,0.0008704184,0.0009745103,0.0006862437,0.0005205703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006060393,"about_ca_system_score_gemma":0.001006005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422937,"about_ca_topic_score_gemma":0.002603035,"domain_scores_codex":[0.9996877,0.00003744565,0.000013488,0.00004733407,0.0001391283,0.00007503841],"domain_scores_gemma":[0.9996225,0.00005057421,0.00005643796,0.000085355,0.000130922,0.00005414769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001099132,0.0003935055,0.004310454,0.00109211,0.0001251417,0.0007870804,0.0001841455,0.2371058,0.1633822,0.02334125,0.05229071,0.5158885],"study_design_scores_gemma":[0.0001968437,0.001359937,0.003276494,0.00007058913,0.0001099334,0.0006966997,0.00007618368,0.8510309,0.07986565,0.00659617,0.05662213,0.0000983926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2726501,0.005122559,0.6550117,0.000941602,0.0006897092,0.0007714754,0.0006853482,0.02753837,0.03658919],"genre_scores_gemma":[0.9294385,0.000636045,0.06380105,0.0003511673,0.00005710136,0.0002460115,0.0008074734,0.0003123792,0.004350255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003223982,"threshold_uncertainty_score":0.01078528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706762900793073,"score_gpt":0.2504595890412046,"score_spread":0.2133919600332738,"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."}}