{"id":"W1982709980","doi":"10.1109/hldvt.2010.5496655","title":"Automatic generation of host-compiled timed TLMs for high level design","year":2010,"lang":"en","type":"article","venue":"","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"University of California, Irvine","keywords":"SystemC; Computer science; Transaction-level modeling; Host (biology); Set (abstract data type); Computer architecture; Instruction set; Parallel computing; Code (set theory); Electronic system-level design and verification; Code generation; Embedded system; Programming language; Key (lock); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008931628,0.0001296006,0.000230728,0.0001278999,0.00006286064,0.0000788846,0.0007385863,0.0001180267,0.00004852999],"category_scores_gemma":[0.0001075444,0.0001088896,0.00005788734,0.0001845809,0.00003191217,0.0003474657,0.00006571317,0.0000842339,0.00002225907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001963337,"about_ca_system_score_gemma":0.00009790101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004332544,"about_ca_topic_score_gemma":0.00001746138,"domain_scores_codex":[0.9988034,0.00009656837,0.0004004794,0.0002808392,0.0002262182,0.0001925367],"domain_scores_gemma":[0.9986581,0.000258508,0.0001775134,0.0006382361,0.0002127986,0.00005487519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001738044,0.00003887858,0.00001107135,0.00002640442,0.00001155877,5.446685e-7,0.0002076815,0.00000843651,0.867394,0.1119064,0.009013298,0.01137998],"study_design_scores_gemma":[0.0001760546,0.0001118635,0.0001514256,0.00000790657,0.000003540359,0.000004761186,0.000002133879,0.3996561,0.5955951,0.004142801,0.00005046374,0.00009786669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02403758,0.000006382078,0.9734619,0.000172443,0.0004142529,0.0009952951,0.000003324195,0.0005609957,0.000347856],"genre_scores_gemma":[0.48896,2.507823e-7,0.510559,0.00005200862,0.00005036776,0.0001015607,0.000002073247,0.000007314311,0.0002674756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4649224,"threshold_uncertainty_score":0.4440388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09589620957974676,"score_gpt":0.290832720823606,"score_spread":0.1949365112438592,"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."}}