{"id":"W2793637056","doi":"10.1109/tcad.2018.2801222","title":"Automatic Application-Specific Calibration to Enable Dynamic Voltage Scaling in FPGAs","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence; Intel Corporation","keywords":"Field-programmable gate array; Computer science; Calibration; Process (computing); Computer hardware; Embedded system; Lookup table; Scaling; Power (physics); Operating system; Mathematics","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":[0.0004477441,0.0003233433,0.0004707118,0.0007050616,0.0001298781,0.0001392929,0.000240092,0.0001852191,0.00002441565],"category_scores_gemma":[0.000002548506,0.0003073415,0.00005355663,0.00100457,0.00006483571,0.0003188992,0.000001421885,0.0002589012,0.00005978847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002698714,"about_ca_system_score_gemma":0.00005523481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001496195,"about_ca_topic_score_gemma":0.00003049537,"domain_scores_codex":[0.9980637,0.0001108808,0.0008020864,0.000398736,0.0002654654,0.0003591067],"domain_scores_gemma":[0.9990261,0.0001680668,0.00009091459,0.000411489,0.0001640197,0.0001394785],"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":[0.00001477198,0.00008471328,0.000009437515,0.0001783904,0.00005871269,0.000004159896,0.001008977,0.8079917,0.1047044,0.0001062923,0.0002821005,0.08555631],"study_design_scores_gemma":[0.0004514056,0.0002877083,0.000055054,0.0004913834,0.00001436596,0.00002162819,0.0001464277,0.9611092,0.03691225,0.00003358339,0.0001811096,0.0002959352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214107,0.0001451734,0.8758456,0.000008676944,0.001119315,0.001013144,0.00002410744,0.0003667702,0.00006651106],"genre_scores_gemma":[0.9966846,0.00005963995,0.00285055,0.00002373814,0.00007710964,0.0001951548,0.000007340042,0.0000622295,0.00003968189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8752739,"threshold_uncertainty_score":0.9999379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517818225319046,"score_gpt":0.2129382866539493,"score_spread":0.1977601044007589,"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."}}