{"id":"W7140600106","doi":"10.1109/fpl68686.2025.00018","title":"FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMS","year":2025,"lang":"","type":"article","venue":"","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Advanced Micro Devices","keywords":"Feature (linguistics); Sequence (biology); Generalization; Matching (statistics)","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":[0.0004739641,0.001266473,0.0003402501,0.0004898025,0.0002173357,0.0008317092,0.002402005,0.0006601479,0.0147587],"category_scores_gemma":[0.001978606,0.0004932708,0.0007184987,0.0003123233,0.0004358293,0.001680319,0.001015746,0.00122786,0.003615188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000905875,"about_ca_system_score_gemma":0.001010766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005508114,"about_ca_topic_score_gemma":0.01045394,"domain_scores_codex":[0.9997151,0.00003697118,0.00003078718,0.00008369915,0.000089917,0.00004359001],"domain_scores_gemma":[0.9996611,0.0001184978,0.00002898324,0.0000986954,0.00007787023,0.00001495105],"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":[0.00129328,0.0001967621,0.00393683,0.001276001,0.0002031724,0.0009134886,0.000471289,0.3143594,0.05372487,0.04322733,0.0548945,0.525503],"study_design_scores_gemma":[0.0001336942,0.0001892591,0.0004730189,0.000104652,0.00004340489,0.0001927246,0.00004231774,0.9070184,0.04396024,0.01733162,0.03046791,0.00004267332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03867402,0.001045819,0.8573701,0.0003399554,0.0003717567,0.0001751747,0.002074685,0.08432617,0.01562236],"genre_scores_gemma":[0.4993019,0.000586407,0.4737033,0.0006388229,0.00008255775,0.0004858449,0.004978798,0.006261943,0.01396051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0147587,"threshold_uncertainty_score":0.04937279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06500321655007829,"score_gpt":0.3677158973722593,"score_spread":0.3027126808221811,"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."}}