{"id":"W2998159825","doi":"","title":"Deep Learning Inference Frameworks for ARM CPU","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Inference; Server; Usability; Enhanced Data Rates for GSM Evolution; Central processing unit; Adaptation (eye); Edge device; Deep learning; Artificial intelligence; Edge computing; Machine learning; Human–computer interaction; Cloud computing; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006956246,0.0001046961,0.0002298657,0.000213306,0.0001183082,0.0004142432,0.0003004544,0.00005546232,0.000003130477],"category_scores_gemma":[0.0001290581,0.00008595621,0.00007230673,0.0001524782,0.00001968258,0.0005113559,0.00007232149,0.0002978952,0.000004826326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002640383,"about_ca_system_score_gemma":0.00005773974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001938241,"about_ca_topic_score_gemma":2.532744e-8,"domain_scores_codex":[0.9988728,0.00009287499,0.0004505476,0.0001397566,0.0003122369,0.0001317576],"domain_scores_gemma":[0.9981029,0.0006345455,0.0004809409,0.00009434934,0.0006104675,0.00007680705],"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":[0.0000118206,0.00003445673,0.002780252,0.00004275466,0.00001535503,0.000002167553,0.0003922343,0.9467404,0.00003611435,0.0213835,0.000678377,0.02788258],"study_design_scores_gemma":[0.000396816,0.0001919732,0.001665952,0.0002356955,0.00000349801,0.0001162343,0.00006728048,0.9860678,0.000009541905,0.00785939,0.003281696,0.000104099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005825947,0.0008507047,0.9916762,0.0005544583,0.0006130521,0.0001308547,2.376743e-7,0.00006621052,0.0002823173],"genre_scores_gemma":[0.8166888,0.00002431102,0.182944,0.0001763015,0.00008220169,0.000001410786,0.000001422868,0.000006335804,0.00007519458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8108629,"threshold_uncertainty_score":0.3994557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008470528831988292,"score_gpt":0.2939385152586868,"score_spread":0.2854679864266985,"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."}}