{"id":"W7133007753","doi":"","title":"Tackling Resource Utilization In Deep Neural Network Accelerators","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Resource (disambiguation); Artificial neural network; Greedy algorithm; Bayesian probability; Bayesian network; Resource allocation; Scheduling (production processes)","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0006141812,0.000870291,0.0007800095,0.0004576706,0.001383982,0.0004149002,0.002508866,0.0004053537,0.0007535808],"category_scores_gemma":[0.000131275,0.001114536,0.0002354928,0.006430794,0.00008739324,0.0007228013,0.0008025805,0.001999447,0.00006860968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005319801,"about_ca_system_score_gemma":0.0002178806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001004285,"about_ca_topic_score_gemma":0.0006414981,"domain_scores_codex":[0.9936237,0.0006347186,0.00121181,0.002075374,0.001020653,0.001433689],"domain_scores_gemma":[0.9961008,0.0005939955,0.0011244,0.001699977,0.000179899,0.0003008933],"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":[0.00009706175,0.0001230283,0.0006916917,0.00006493206,0.00001993808,0.00004359178,0.01094299,0.9041638,0.0001595204,0.007271154,0.0009759317,0.07544637],"study_design_scores_gemma":[0.0005694316,0.000142608,0.003319475,0.000126614,0.00004495678,0.00001885463,0.004194784,0.9274474,0.0002504539,0.001496831,0.06122868,0.001159912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.42103,0.02514021,0.503574,0.004763906,0.009771702,0.008842667,0.00001700612,0.00181454,0.02504597],"genre_scores_gemma":[0.9640337,0.001193434,0.01713438,0.001689798,0.001588022,0.001393057,0.001846401,0.0003596677,0.01076151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5430037,"threshold_uncertainty_score":0.9999161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04158321525887822,"score_gpt":0.3535297073812612,"score_spread":0.311946492122383,"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."}}