{"id":"W2143226612","doi":"10.5555/1950815.1950911","title":"Register pressure aware scheduling for high level synthesis","year":2011,"lang":"en","type":"article","venue":"Asia and South Pacific Design Automation Conference","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Register allocation; Computer science; Instruction scheduling; High-level synthesis; Parallel computing; Scheduling (production processes); Serialization; Optimizing compiler; Compiler; Data-flow analysis; Two-level scheduling; Dynamic priority scheduling; Schedule; Embedded system; Field-programmable gate array; Programming language; Mathematical optimization; Operating system; Data flow diagram; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005181254,0.0005134431,0.000320957,0.000482989,0.0003556479,0.0007856312,0.0006784875,0.0003356998,0.003013542],"category_scores_gemma":[0.001179128,0.0002676336,0.0003730742,0.0005841996,0.000496986,0.0008817905,0.0005892933,0.0007236649,0.0006170174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007535887,"about_ca_system_score_gemma":0.001217864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001091879,"about_ca_topic_score_gemma":0.002720881,"domain_scores_codex":[0.9995603,0.0001342164,0.00002333219,0.00005036141,0.0001738482,0.00005792772],"domain_scores_gemma":[0.9995535,0.0002137831,0.00005790087,0.0001051021,0.00005292427,0.0000167976],"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":[0.0002015018,0.0000981148,0.0004843979,0.0002314258,0.00002592996,0.0000792464,0.00009082335,0.6165175,0.04956578,0.06188927,0.002949569,0.2678663],"study_design_scores_gemma":[0.0000237687,0.00009865258,0.0001895018,0.00001634559,0.00001443893,0.00002753811,0.00001824653,0.9478773,0.01584218,0.03070282,0.005177722,0.00001154192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0158624,0.0003983326,0.9776945,0.0001247812,0.00003318832,0.00003728881,0.00004064264,0.001200519,0.004608305],"genre_scores_gemma":[0.5317041,0.0006551611,0.4622766,0.0001073318,0.00008386685,0.0001645488,0.0001620021,0.0003966077,0.004449754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003013542,"threshold_uncertainty_score":0.01008135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130551031710665,"score_gpt":0.26002247880503,"score_spread":0.1469673756339635,"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."}}