{"id":"W2725753878","doi":"10.1145/3060579","title":"Improving Performance under Process and Voltage Variations in Near-Threshold Computing Using 3D ICs","year":2017,"lang":"en","type":"article","venue":"ACM Journal on Emerging Technologies in Computing Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Voltage; Threshold voltage; Die (integrated circuit); Three-dimensional integrated circuit; Chip; Voltage drop; Process corners; Integrated circuit; Transistor; Electronic engineering; Power network design; Integrated circuit design; Engineering; Low voltage; Sensitivity (control systems); Power (physics); Process variation; Electrical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001055089,0.0003283525,0.0004576789,0.0004315563,0.001382499,0.0005165792,0.0010222,0.0002119915,3.674084e-7],"category_scores_gemma":[0.0006156701,0.0003308681,0.00004212313,0.00033495,0.000135033,0.0005406287,0.0005977912,0.001562734,0.000001070792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003086005,"about_ca_system_score_gemma":0.00003773782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001891816,"about_ca_topic_score_gemma":0.000004404792,"domain_scores_codex":[0.9978738,0.00003428258,0.0007870636,0.0003706248,0.0002595837,0.0006746075],"domain_scores_gemma":[0.9984974,0.0002462907,0.0005063338,0.0006320197,0.00006845409,0.00004952335],"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":[0.000006451658,0.00001454477,0.03114358,0.0002425984,0.00001448494,0.00006059838,0.0003115633,0.9277657,0.002239713,0.0001748977,0.000002701244,0.03802322],"study_design_scores_gemma":[0.0005203024,0.00004962644,0.003691098,0.00192427,0.000007734601,0.0002861524,0.0009794259,0.9906803,0.001029225,0.0004528863,0.00001886496,0.0003600958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9214214,0.0006340634,0.07550524,0.00008635056,0.001410797,0.0002094865,7.447535e-7,0.0006454913,0.0000863928],"genre_scores_gemma":[0.9919993,0.00007986835,0.007681824,0.0000124176,0.0001692475,0.000001917559,4.462947e-7,0.00005189551,0.000003142499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07057781,"threshold_uncertainty_score":0.9999176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03254818440203598,"score_gpt":0.2986155316724781,"score_spread":0.2660673472704421,"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."}}