{"id":"W2796256231","doi":"10.1109/tvlsi.2018.2819896","title":"Feedback-Based Low-Power Soft-Error-Tolerant Design for Dual-Modular Redundancy","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; China Scholarship Council","keywords":"Triple modular redundancy; Redundancy (engineering); Soft error; Computer science; Modular design; Error detection and correction; Overhead (engineering); Majority rule; Voting; Fault tolerance; Algorithm; Electronic engineering; Engineering; Artificial intelligence; Distributed computing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008003456,0.000520909,0.0005099908,0.0004355052,0.0005160113,0.0002030025,0.0002582067,0.0004181408,0.0001457053],"category_scores_gemma":[0.00002926579,0.0005239303,0.000318138,0.0005660299,0.0001019547,0.0004943053,8.492485e-7,0.0004916621,0.0003236523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007329751,"about_ca_system_score_gemma":0.0001718873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000249168,"about_ca_topic_score_gemma":0.0001530367,"domain_scores_codex":[0.9971008,0.000246639,0.0007968206,0.0005655614,0.0005119897,0.00077817],"domain_scores_gemma":[0.9981782,0.0003644184,0.0001423616,0.0007139206,0.0004080204,0.0001930976],"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":[0.0001600686,0.0002222498,0.000001382865,0.0001484876,0.000129484,0.000002186364,0.0005739242,0.9813354,0.01269896,0.0001268841,0.00282095,0.001780006],"study_design_scores_gemma":[0.001322347,0.0005342101,0.00001379445,0.0002901953,0.00007030759,0.0000129594,0.0001874414,0.8830929,0.1085403,0.00003995317,0.005399409,0.0004961594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04390514,0.0001982992,0.9454534,0.00008192816,0.006991538,0.001864741,0.0002410769,0.0009385442,0.0003253353],"genre_scores_gemma":[0.99497,0.000008033036,0.002627555,0.0001192554,0.0004086942,0.0009709309,0.00004730773,0.0001769077,0.0006712966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9510649,"threshold_uncertainty_score":0.9997212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110167567305611,"score_gpt":0.2341114578287062,"score_spread":0.2230097821556501,"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."}}