{"id":"W2138285178","doi":"10.1109/ccece.2007.114","title":"Digital Emulation of Analogue CNN System on FPGA","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Emulation; Computer science; Template; Field-programmable gate array; Cellular neural network; Realization (probability); Hardware emulation; Computer hardware; Embedded system; Chip; Computer architecture; Artificial neural network; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001552186,0.0002311908,0.0001337242,0.0001597688,0.0001006423,0.0002257502,0.0003755243,0.000157207,0.003224994],"category_scores_gemma":[0.0005147478,0.00005708054,0.0001208196,0.0001127174,0.0001617884,0.0003239154,0.0001587866,0.0001404386,0.0002285533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616808,"about_ca_system_score_gemma":0.0001688046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099969,"about_ca_topic_score_gemma":0.0007167195,"domain_scores_codex":[0.9998895,0.00002382113,0.00000837351,0.00001744358,0.00004660883,0.00001428789],"domain_scores_gemma":[0.9998608,0.00004257365,0.00001901823,0.00003824997,0.00003408589,0.00000517511],"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":[0.0006285489,0.00006992407,0.00425082,0.0003533618,0.00005950689,0.0008720564,0.0003270299,0.6472672,0.1402654,0.033223,0.002829721,0.1698534],"study_design_scores_gemma":[0.00004139629,0.0002232152,0.001316027,0.00002128449,0.00002296357,0.0002509372,0.00003268226,0.8931653,0.09640559,0.001952595,0.006554893,0.00001315233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3142959,0.000192898,0.6578184,0.0001889948,0.0001313023,0.0001080812,0.0002060753,0.002476759,0.02458156],"genre_scores_gemma":[0.968219,0.00005850787,0.02928788,0.00003007772,0.000005551406,0.00003676984,0.00006701043,0.00003080767,0.002264363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003224994,"threshold_uncertainty_score":0.01078868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337818063898355,"score_gpt":0.2261931814886416,"score_spread":0.2128150008496581,"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."}}