{"id":"W4312001641","doi":"10.5121/csit.2022.122204","title":"A Memory Based Approach for Digital Implementation of Tanh using LUT and RALUT","year":2022,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; Lookup table; Computer science; Field-programmable gate array; Table (database); Artificial neural network; Activation function; Function (biology); Differentiable function; Range (aeronautics); Schematic; Backpropagation; Algorithm; Arithmetic; Parallel computing; Computer engineering; Computer hardware; Artificial intelligence; Mathematics; Electronic engineering; Data mining","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":[0.0002215988,0.0004599375,0.0002374143,0.0006384501,0.0002514993,0.0008644596,0.001209082,0.0003266858,0.01061373],"category_scores_gemma":[0.0005946154,0.0001950724,0.0002540161,0.0006888565,0.0002134548,0.0008280034,0.0002552511,0.0004989435,0.002070782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003720492,"about_ca_system_score_gemma":0.0004216772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008184244,"about_ca_topic_score_gemma":0.00187555,"domain_scores_codex":[0.9998179,0.00002907012,0.0000224708,0.0000363846,0.00007065699,0.00002344075],"domain_scores_gemma":[0.9997644,0.00005853493,0.00003035987,0.00006440592,0.00007649049,0.000005772222],"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":[0.0003280038,0.00009735395,0.0009318464,0.001237692,0.00009719642,0.0005247041,0.0002549398,0.01192196,0.2342192,0.04212619,0.008002258,0.7002587],"study_design_scores_gemma":[0.0001208893,0.001831428,0.002178267,0.0002840833,0.0002343789,0.004628196,0.0002297012,0.1545121,0.6219385,0.008499565,0.2054325,0.000110288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03876211,0.004230477,0.9219361,0.0003753228,0.0005340004,0.0002248921,0.0003635567,0.007309769,0.02626378],"genre_scores_gemma":[0.5275925,0.001615307,0.4482835,0.0004611535,0.0001245993,0.0001859424,0.0004289273,0.0002198046,0.02108834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01061373,"threshold_uncertainty_score":0.03550649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217888847485779,"score_gpt":0.2908276819422849,"score_spread":0.2586487934674271,"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."}}