{"id":"W2842591198","doi":"10.1109/tcad.2018.2855168","title":"A Lifetime Reliability-Constrained Runtime Mapping for Throughput Optimization in Many-Core Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Nova; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Throughput; Reliability (semiconductor); Constraint (computer-aided design); Computation; Distributed computing; Multi-core processor; Task (project management); Time constraint; Scheme (mathematics); Computer engineering; Parallel computing; Algorithm; Power (physics); Engineering; Wireless; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001383083,0.0003958927,0.0007255103,0.000722345,0.0002912395,0.0003385146,0.0006434756,0.0002834712,0.000004061111],"category_scores_gemma":[0.00002948695,0.0003636243,0.0001248669,0.001066174,0.0001777587,0.0004295135,0.000007736654,0.0002648435,0.000004730542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547734,"about_ca_system_score_gemma":0.0002130956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002627893,"about_ca_topic_score_gemma":0.00000208968,"domain_scores_codex":[0.9968218,0.0003995426,0.001179243,0.0008360547,0.0003233961,0.0004400245],"domain_scores_gemma":[0.9973968,0.0005860304,0.0004154006,0.0006592804,0.0008121009,0.0001303807],"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":[0.0000317711,0.0001834252,0.00001171431,0.0001539379,0.00006164559,0.000003868308,0.0007474279,0.9866778,0.000702014,0.002414617,0.0002678322,0.008744],"study_design_scores_gemma":[0.0009459332,0.0009225453,0.0000117538,0.0008814568,0.00001660106,0.00006017226,0.0001073183,0.995055,0.00136534,0.0001650992,0.0001098114,0.0003589523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001211486,0.0001403999,0.9945511,0.00006109104,0.001484908,0.001848144,0.00003079345,0.0005283202,0.0001437673],"genre_scores_gemma":[0.7739844,0.00004788366,0.225477,0.00006050347,0.0001079776,0.0001716574,0.000007704294,0.00003134969,0.0001115921],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7727728,"threshold_uncertainty_score":0.9998816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04215419102446877,"score_gpt":0.2596485821550559,"score_spread":0.2174943911305871,"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."}}