{"id":"W2140479193","doi":"10.1109/icnn.1996.549011","title":"A modular architecture for hybrid VLSI neural networks and its application in a smart photosensor","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Scalability; Modular design; Block (permutation group theory); Neuromorphic engineering; CMOS; Robustness (evolution); Computer architecture; Artificial neural network; Computer hardware; Embedded system; Electronic engineering; Engineering; 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":[0.0001245678,0.0003339307,0.000338401,0.000190342,0.00007948583,0.00007660061,0.0003527038,0.0001094009,0.00001824703],"category_scores_gemma":[0.00006620305,0.0003369706,0.00009339993,0.0002062131,0.00005016009,0.0003086255,0.00007763746,0.0005535888,0.000001285576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004967363,"about_ca_system_score_gemma":0.000002475332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002708634,"about_ca_topic_score_gemma":0.000004875969,"domain_scores_codex":[0.9983984,0.000008093665,0.0004735268,0.0004686528,0.0002276468,0.000423731],"domain_scores_gemma":[0.9992616,0.000121563,0.0001726135,0.00008558665,0.0002459712,0.0001127056],"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":[0.0002467578,0.00007579323,0.0008219786,0.0001333465,0.00004296306,0.000006730282,0.0001344984,0.9202517,0.03537028,0.003471277,0.0001847465,0.03925996],"study_design_scores_gemma":[0.000766606,0.0001448355,0.0003382476,0.0001313482,0.00001130721,0.0000487486,0.00002958479,0.9895316,0.007827707,0.0006420364,0.0002286224,0.0002993314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884983,0.0004144874,0.008247839,0.0005435751,0.0003936757,0.0007721785,0.00001541688,0.0001835735,0.0009309108],"genre_scores_gemma":[0.9985564,0.0002638586,0.0003027118,0.0002210792,0.0003863779,0.0001307934,0.00001961383,0.00004808086,0.00007106529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06927995,"threshold_uncertainty_score":0.9999082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02436856128460841,"score_gpt":0.2384320765011076,"score_spread":0.2140635152164992,"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."}}