{"id":"W3215002812","doi":"10.1109/icfpt52863.2021.9609816","title":"Profiling-Based Control-Flow Reduction in High-Level Synthesis","year":2021,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Control flow; Control flow graph; Parallel computing; Field-programmable gate array; High-level synthesis; Benchmark (surveying); Profiling (computer programming); Suite; Graph; Electronic circuit; Software; Embedded system; Theoretical computer science; Programming language; Engineering","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":[0.0002858424,0.00008874307,0.0001426576,0.0001156114,0.00005580173,0.00009449617,0.0002786198,0.00005966914,0.00002525575],"category_scores_gemma":[0.0001545248,0.00008573978,0.00003966911,0.0004619762,0.0000164312,0.0001623816,0.00003756697,0.00008511914,0.00001460347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004380431,"about_ca_system_score_gemma":0.0001543578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000268427,"about_ca_topic_score_gemma":0.000006359922,"domain_scores_codex":[0.9990281,0.000130301,0.0002012989,0.0003225752,0.0001479508,0.0001697387],"domain_scores_gemma":[0.9993057,0.0001120146,0.00004988003,0.0003600806,0.0001329582,0.00003939879],"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":[0.0000346688,0.0005800244,0.002732561,0.00004910474,0.00003231996,0.0001002773,0.0001218425,0.8045545,0.007810435,0.08986144,0.005121462,0.08900134],"study_design_scores_gemma":[0.0002769811,0.00001489632,0.0006717175,0.0000228942,0.000002115601,0.000007476776,0.00000463009,0.8422759,0.155479,0.001031479,0.00009559117,0.0001173289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00227551,0.00003602749,0.9929728,0.002160543,0.0001640545,0.00008686409,0.000001645249,0.0004918471,0.001810706],"genre_scores_gemma":[0.5019968,0.000003286238,0.4975709,0.0002020094,0.00001899913,0.00001678107,0.000002101007,0.000003499674,0.0001856573],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4997213,"threshold_uncertainty_score":0.3496367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065617816049631,"score_gpt":0.2440982273169967,"score_spread":0.2234420491565004,"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."}}