{"id":"W2897469144","doi":"10.3390/computers7040052","title":"Run-Time Mitigation of Power Budget Variations and Hardware Faults by Structural Adaptation of FPGA-Based Multi-Modal SoPC","year":2018,"lang":"en","type":"article","venue":"Computers","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Control reconfiguration; Field-programmable gate array; Embedded system; Computer science; Task (project management); Power budget; Adaptation (eye); Process (computing); Reconfigurable computing; Power (physics); Computer hardware; Transient (computer programming); Real-time computing; Electric power system; Engineering","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":[0.0001008192,0.0003651931,0.0001494969,0.0002660283,0.0001383669,0.0002029676,0.0005218273,0.0001564508,0.0009217783],"category_scores_gemma":[0.0004767608,0.0001094301,0.0001366059,0.0001303775,0.0001461428,0.0002378434,0.0002176861,0.0002201934,0.0001164982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002146177,"about_ca_system_score_gemma":0.0002326012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000744777,"about_ca_topic_score_gemma":0.001612361,"domain_scores_codex":[0.9998659,0.00002169396,0.00000777143,0.00003383597,0.00004055363,0.00003034293],"domain_scores_gemma":[0.9997085,0.00006011567,0.00007823763,0.0000834006,0.00005351349,0.00001627158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005271853,0.0001679499,0.00387027,0.00015568,0.00005332177,0.0002993899,0.0001637923,0.2284747,0.4806314,0.001177094,0.0007329371,0.2837463],"study_design_scores_gemma":[0.00002834599,0.0006696327,0.008804541,0.00001638054,0.00006487855,0.00037235,0.00006487162,0.7748289,0.2098838,0.0008203173,0.004421322,0.00002475736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6709257,0.000475243,0.3201346,0.00009617863,0.00006857829,0.0001209341,0.00005457427,0.002892507,0.005231683],"genre_scores_gemma":[0.9836215,0.00004028866,0.01573753,0.00001888414,0.000005424056,0.00001582685,0.00002263749,0.00002739364,0.00051067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009217783,"threshold_uncertainty_score":0.003083706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202581298719503,"score_gpt":0.2497208750328546,"score_spread":0.2376950620456596,"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."}}