{"id":"W2733045287","doi":"10.4230/lipics.ecrts.2017.24","title":"WCET-Driven Dynamic Data Scratchpad Management With Compiler-Directed Prefetching","year":2017,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Compiler; Worst-case execution time; Parallel computing; Latency (audio); CAS latency; Scheme (mathematics); Embedded system; Operating system; Execution time; Memory controller; Semiconductor memory","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":[0.0004745419,0.0006109497,0.0004754738,0.0007440644,0.000331206,0.000633528,0.0009915078,0.000344925,0.001319624],"category_scores_gemma":[0.001794766,0.0003234559,0.0002673123,0.0006485587,0.0007482465,0.0008829467,0.0006247882,0.0005699092,0.0002555514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006089485,"about_ca_system_score_gemma":0.001480723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003764008,"about_ca_topic_score_gemma":0.005255542,"domain_scores_codex":[0.9994082,0.00009178363,0.00003917469,0.0001021836,0.0002179293,0.000140708],"domain_scores_gemma":[0.9988748,0.0004349997,0.0001662048,0.0003019172,0.0001878546,0.00003410379],"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":[0.0006865427,0.0002964091,0.006550299,0.0002862664,0.00006563255,0.0004492138,0.0003041295,0.5502403,0.1924649,0.01595294,0.004404411,0.228299],"study_design_scores_gemma":[0.00003081174,0.0000803712,0.0005395439,0.000008726295,0.00001861848,0.00004187692,0.00001281795,0.926584,0.0687982,0.002636956,0.001233698,0.00001439169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3502134,0.0006208817,0.6262094,0.0002880656,0.00009815175,0.0001037158,0.0001630244,0.01843996,0.003863239],"genre_scores_gemma":[0.8750478,0.000118725,0.1221479,0.00007524852,0.0000227471,0.00007964603,0.0001314412,0.0006398488,0.001736623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003764008,"threshold_uncertainty_score":0.007484198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026048546202369,"score_gpt":0.298696953606814,"score_spread":0.2684364681447903,"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."}}