{"id":"W4391010069","doi":"","title":"Polyhedra at Work: Automatic Generation of VHDL Code for the Sherman-Morrison Formula","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; CMC Microsystems","keywords":"Polyhedron; Computer science; VHDL; Code generation; Code (set theory); Programming language; Parallel computing; Computer architecture; Operating system; Combinatorics; Field-programmable gate array; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004887627,0.001121497,0.0004641024,0.0008220264,0.0005789713,0.001271376,0.001496207,0.0005775319,0.01728446],"category_scores_gemma":[0.002022253,0.0005386242,0.00078253,0.0005771756,0.000548606,0.0008987449,0.001039899,0.001210995,0.005177288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006714272,"about_ca_system_score_gemma":0.00129948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614578,"about_ca_topic_score_gemma":0.003454101,"domain_scores_codex":[0.9994494,0.00009236645,0.00003729103,0.00008048373,0.0002469764,0.00009346655],"domain_scores_gemma":[0.9991379,0.0003484879,0.00006750273,0.0001529513,0.0002569667,0.00003622055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001299398,0.0003492007,0.002699821,0.0008964996,0.0001054044,0.001131531,0.001148696,0.1179861,0.1340722,0.08404578,0.03218644,0.6240789],"study_design_scores_gemma":[0.0002953652,0.0002281333,0.0004392711,0.0001547282,0.00007649019,0.0003037622,0.0001973625,0.6956015,0.2203702,0.03082047,0.05142578,0.00008696302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02080078,0.0000768028,0.9502297,0.00008983355,0.00008980828,0.0001444104,0.0003837823,0.01952948,0.008655339],"genre_scores_gemma":[0.3503336,0.000196251,0.6281275,0.0001886465,0.00004478265,0.0003404657,0.002065204,0.008467242,0.01023626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01728446,"threshold_uncertainty_score":0.05782223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122578550176634,"score_gpt":0.2627137124301067,"score_spread":0.2314879269283403,"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."}}