{"id":"W4405937423","doi":"10.1109/iccais63750.2024.10814271","title":"Anomaly Detection in Dynamic Power Events Using Data Fusion for Chip Design","year":2024,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada); McMaster University","funders":"","keywords":"Anomaly detection; Chip; Computer science; System on a chip; Sensor fusion; Power (physics); Embedded system; Data mining; Artificial intelligence; Telecommunications; Physics","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":[0.001416225,0.0009766184,0.001167858,0.002908146,0.0004075634,0.0008047823,0.001101964,0.0008160337,0.0005191599],"category_scores_gemma":[0.005009894,0.0003035598,0.001054475,0.00230453,0.0004457586,0.001123392,0.0009264942,0.0009196733,0.0003177305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006552946,"about_ca_system_score_gemma":0.0007556572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001630886,"about_ca_topic_score_gemma":0.001463935,"domain_scores_codex":[0.9984499,0.0002359679,0.0001521635,0.0004177673,0.0006120547,0.0001321684],"domain_scores_gemma":[0.9976282,0.001016827,0.0004153805,0.0003358755,0.000530125,0.00007357576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004146365,0.0004000082,0.01958353,0.0002286475,0.000222319,0.0003295578,0.0002291085,0.2729288,0.02974032,0.003442636,0.001866896,0.6706135],"study_design_scores_gemma":[0.000008107629,0.0001381757,0.003435576,0.00001359361,0.00003093264,0.000138619,0.0000302249,0.9785048,0.01266883,0.003464813,0.00154812,0.00001824617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03743069,0.0004789544,0.959338,0.0001279236,0.00004894233,0.00008321846,0.0002824519,0.001729669,0.0004801207],"genre_scores_gemma":[0.6366689,0.0002750763,0.3612895,0.00008244529,0.00006010317,0.0001774623,0.0009015084,0.00005362209,0.0004913072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002908146,"threshold_uncertainty_score":0.0074898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08861643092463771,"score_gpt":0.318515474037961,"score_spread":0.2298990431133233,"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."}}