{"id":"W4415220943","doi":"10.1145/3787490","title":"wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation","year":2025,"lang":"en","type":"report","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Canada Research Chairs; Alfred P. Sloan Foundation; Office of the President, University of California; U.S. Department of Energy","keywords":"Benchmark (surveying); Latency (audio); Limiting; Field-programmable gate array; Resource (disambiguation); Percentile; Surrogate model","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008732849,0.0004010845,0.0008118005,0.001124676,0.0005513007,0.0002251356,0.0006328656,0.001120395,0.000003635029],"category_scores_gemma":[0.0002032302,0.0003647324,0.00008486855,0.0004841299,0.0002302941,0.0003116812,0.000029889,0.0005608706,0.000001623525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007202897,"about_ca_system_score_gemma":0.0002546011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003582668,"about_ca_topic_score_gemma":0.000113033,"domain_scores_codex":[0.9976904,0.00007696115,0.0006190278,0.0009680801,0.0002281031,0.0004173716],"domain_scores_gemma":[0.9976712,0.0006573741,0.0002866565,0.001066626,0.0002405293,0.00007761979],"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":[0.00008092653,0.0001446417,0.0002113982,0.004008446,0.0007298125,0.00004802924,0.0005140431,0.003795929,0.0001112138,0.05089696,0.003929026,0.9355296],"study_design_scores_gemma":[0.002428437,0.001129547,0.0002772753,0.004238855,0.0006613218,0.002413464,0.001427075,0.7485977,0.001267184,0.1550397,0.08068693,0.001832473],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005259488,0.025351,0.9436581,0.00521035,0.002003358,0.002219906,0.0001515497,0.001204976,0.01494133],"genre_scores_gemma":[0.9422247,0.0146135,0.02185519,0.0001119913,0.00005946215,0.001238723,0.00005835004,0.00005384144,0.01978423],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9369652,"threshold_uncertainty_score":0.9998805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03832620855967011,"score_gpt":0.277063652340106,"score_spread":0.2387374437804359,"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."}}