{"id":"W2987526256","doi":"10.1109/fpl.2019.00011","title":"Becoming More Tolerant: Designing FPGAs for Variable Supply Voltage","year":2019,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Lookup table; Field-programmable gate array; Voltage; Computer science; Routing (electronic design automation); Scaling; Dynamic voltage scaling; Energy consumption; Power (physics); Low-power electronics; Embedded system; Power consumption; Electrical engineering; Engineering; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002797645,0.0002456218,0.0002887759,0.0001313651,0.00008443319,0.00008166306,0.0002573385,0.0001235441,0.0008671826],"category_scores_gemma":[0.00001535239,0.0002346183,0.00007352392,0.0002128555,0.0000141605,0.0004839888,0.00003246584,0.0001594341,0.0004413631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009319357,"about_ca_system_score_gemma":0.00002943796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002569257,"about_ca_topic_score_gemma":0.000002482456,"domain_scores_codex":[0.9987132,0.000007229695,0.000285816,0.0002607802,0.0001646045,0.0005683723],"domain_scores_gemma":[0.9993135,0.0001596648,0.00002763286,0.0003605949,0.00004623885,0.00009233315],"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":[0.00005752164,0.000052863,0.00672752,0.0009758416,0.0002023488,0.000009111424,0.002175657,0.5660031,0.3803072,0.00522127,0.03437556,0.003891942],"study_design_scores_gemma":[0.001435181,0.00009346351,0.000437793,0.000103945,0.00003354315,0.00001028898,0.0002801089,0.8413723,0.1216469,0.0002815571,0.03371033,0.000594596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3054811,0.0001957476,0.6713705,0.00005238982,0.001297096,0.0009896462,0.00001833223,0.001029033,0.01956617],"genre_scores_gemma":[0.9247188,0.000009711017,0.07008297,0.000181058,0.0001517739,0.00007216585,0.00002170282,0.0001087942,0.004653024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6192377,"threshold_uncertainty_score":0.9567455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008547642549954304,"score_gpt":0.205383321134714,"score_spread":0.1968356785847597,"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."}}