{"id":"W1578246419","doi":"10.1109/isvlsi.2005.18","title":"Analysis of Incremental Communication for Multilayer Neural Networks on a Field Programmable Gate Array","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial neural network; Field-programmable gate array; Massively parallel; Telecommunications network; Network architecture; Distributed computing; Computer architecture; Computer hardware; Computer network; Parallel computing; Artificial intelligence","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":[0.0006415527,0.0003689273,0.0002680639,0.0003961218,0.0003392023,0.0004429049,0.0007823044,0.0004280385,0.003364027],"category_scores_gemma":[0.004087701,0.0002121504,0.0003465009,0.000290807,0.0004478951,0.001000276,0.0003665483,0.0006239842,0.0001928504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263362,"about_ca_system_score_gemma":0.000888388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004579178,"about_ca_topic_score_gemma":0.004514301,"domain_scores_codex":[0.9996608,0.00006458745,0.00001178554,0.00003531256,0.0001653275,0.00006211321],"domain_scores_gemma":[0.9979934,0.001375573,0.0001523452,0.00008899211,0.0003484787,0.00004129777],"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":[0.0001266933,0.00003253894,0.0008343557,0.0001012227,0.00002090071,0.0001544545,0.00007788079,0.9220376,0.005706293,0.03484883,0.0008068614,0.03525233],"study_design_scores_gemma":[0.000001998329,0.00001948795,0.0001173167,0.00000235973,0.000004220505,0.00001825219,0.000004048583,0.9965549,0.0006746394,0.002447882,0.0001528914,0.000001985749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1591863,0.0005514447,0.8238807,0.0004038755,0.00005687695,0.0000776554,0.00006050918,0.0005691388,0.01521368],"genre_scores_gemma":[0.9634684,0.0002575683,0.03252973,0.0000663231,0.00002928637,0.00007172544,0.00004975322,0.00005280256,0.003474422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004579178,"threshold_uncertainty_score":0.01125377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193062937349195,"score_gpt":0.2894317272328995,"score_spread":0.2675010978594076,"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."}}