{"id":"W2100717332","doi":"10.1109/tnn.2010.2073481","title":"High-Performance Reconfigurable Hardware Architecture for Restricted Boltzmann Machines","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Speedup; Modular design; Exploit; Parallel computing; Virtex; Embedded system; Artificial neural network; Reconfigurable computing; Computer architecture; Hardware acceleration; Hardware architecture; Computer hardware; Software; Operating system; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001370971,0.0003207985,0.0002895582,0.0001463476,0.0006476154,0.0002289339,0.0007325865,0.0001944122,0.00006225833],"category_scores_gemma":[0.00001077247,0.0002717698,0.0001802092,0.0005121542,0.00007149666,0.0004991217,0.000003370235,0.0008419091,0.00001175414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001804763,"about_ca_system_score_gemma":0.00003018443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006078125,"about_ca_topic_score_gemma":0.00019458,"domain_scores_codex":[0.9982708,0.00007848629,0.0003173951,0.0005947618,0.0001920723,0.000546512],"domain_scores_gemma":[0.998606,0.0003070417,0.0001105066,0.0006802403,0.0001342364,0.0001620273],"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":[0.00006174138,0.00004967469,0.000004383847,0.000006835396,0.00002555785,0.000002497729,0.00002763014,0.7290766,0.003187996,0.00008056215,0.001387639,0.2660889],"study_design_scores_gemma":[0.0005257236,0.0002605143,0.0002091721,0.00001817107,0.00002455875,0.0000214964,0.000002010561,0.9737272,0.02139449,0.0001890677,0.003301231,0.00032633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0259555,0.0000320233,0.9661574,0.001481879,0.005419212,0.0004764911,0.00002615278,0.0002869631,0.0001643684],"genre_scores_gemma":[0.9698671,0.00004610645,0.02793005,0.0006141602,0.0006291925,0.0001392168,0.000008884874,0.00003381474,0.0007315279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9439116,"threshold_uncertainty_score":0.9999735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109397436070237,"score_gpt":0.2180030602028318,"score_spread":0.2069090858421294,"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."}}