{"id":"W4384833556","doi":"10.1145/3597031.3597057","title":"cuSCNN : an Efficient CUDA Implementation of Sparse CNNs","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speedup; Parallel computing; CUDA; Sparse matrix; Convolutional neural network; Inference; Computation; Memory bandwidth; Kernel (algebra); Latency (audio); Throughput; Computer engineering; Algorithm; Artificial intelligence","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":[0.0001028189,0.00005795169,0.00006463112,0.0000760403,0.00006424708,0.00001792219,0.0003983426,0.00001336998,0.00003666614],"category_scores_gemma":[0.000003250316,0.00005273091,0.00002345522,0.0008942612,0.0000184224,0.000166752,0.0001526939,0.00003241808,0.0001385377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001415059,"about_ca_system_score_gemma":0.00002030885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002968625,"about_ca_topic_score_gemma":0.0000489189,"domain_scores_codex":[0.9992607,0.00001950592,0.0001667727,0.0002251284,0.0001579449,0.0001699924],"domain_scores_gemma":[0.9993492,0.00004564424,0.00006070216,0.0004455146,0.00004297396,0.0000559445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002438255,0.0001107306,0.002107602,0.000009999549,0.000009391104,0.000004266786,0.001165554,0.1112075,0.01699001,0.6484839,0.004319845,0.2155887],"study_design_scores_gemma":[0.0004287419,0.0001427036,0.04233726,0.000005199805,0.000005824822,0.00000348696,0.0004899009,0.8842755,0.05348263,0.0126095,0.005968453,0.0002507972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3275204,0.000004412723,0.6702353,0.0007827972,0.00009269769,0.0002107262,0.000003181897,0.0003794417,0.0007710252],"genre_scores_gemma":[0.9615744,0.000006855687,0.03807129,0.0001067723,0.00002531454,0.0000345117,0.00001046477,0.000005296048,0.0001651389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.773068,"threshold_uncertainty_score":0.2150304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867691439024008,"score_gpt":0.3471304006101584,"score_spread":0.3084534862199183,"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."}}