{"id":"W2768414512","doi":"","title":"Work-in-progress: heterogeneous redundancy to address performance and cost in multi-core SIMT","year":2017,"lang":"en","type":"article","venue":"International Conference on Hardware/Software Codesign and System Synthesis","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Redundancy (engineering); Computer science; Core (optical fiber); Yield (engineering); Many core; Parallel computing; Distributed computing; Computer architecture; Telecommunications; Materials science; Operating system","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":[0.001302281,0.0009429348,0.0004856347,0.0005726311,0.0006380992,0.00104568,0.00269456,0.0006943839,0.006369527],"category_scores_gemma":[0.001886124,0.0002057165,0.0003555998,0.0006307422,0.0006131548,0.002516835,0.001383763,0.0008384263,0.00115629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004245141,"about_ca_system_score_gemma":0.0007039486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004417234,"about_ca_topic_score_gemma":0.0009159824,"domain_scores_codex":[0.9995043,0.0001110631,0.00001702147,0.00008368741,0.0001934661,0.00009042781],"domain_scores_gemma":[0.9990047,0.0002223629,0.0000776169,0.000369511,0.0002449879,0.00008083213],"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":[0.000497853,0.0004259888,0.001813425,0.0006125554,0.0001330397,0.0003707289,0.0005071213,0.09515864,0.07245268,0.1307881,0.02495909,0.6722808],"study_design_scores_gemma":[0.0001030421,0.001461514,0.001161813,0.0001996244,0.0001899704,0.0007503877,0.0003806867,0.7318742,0.08127217,0.0763514,0.1061824,0.0000727642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1998073,0.01752185,0.6932971,0.004300766,0.001222162,0.000311316,0.0003761238,0.003362955,0.07980035],"genre_scores_gemma":[0.8209959,0.003082665,0.1586968,0.0006955522,0.0005334634,0.0001205223,0.0003650986,0.0004081438,0.01510174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006369527,"threshold_uncertainty_score":0.02130818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049950198191093,"score_gpt":0.3262458722754042,"score_spread":0.2212508524562949,"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."}}