{"id":"W4311119077","doi":"10.1145/3570305","title":"YaConv: Convolution with Low Cache Footprint","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Architecture and Code Optimization","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Cache; Speedup; Memory footprint; Parallel computing; Convolution (computer science); CPU cache; Memory hierarchy; Reduction (mathematics); Cache algorithms; Algorithm; Computer engineering; Computational science; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005038523,0.0007182105,0.0006095452,0.000505026,0.0005961703,0.001204722,0.002434988,0.0005919278,0.004979712],"category_scores_gemma":[0.001672729,0.0004714693,0.0005707189,0.0007335896,0.0006228856,0.001599262,0.001499391,0.001139364,0.001916704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133754,"about_ca_system_score_gemma":0.002088633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00620874,"about_ca_topic_score_gemma":0.01263796,"domain_scores_codex":[0.99939,0.00007066124,0.00002692336,0.00009431707,0.0003107097,0.0001073855],"domain_scores_gemma":[0.9995433,0.0001152947,0.00003082639,0.0001485832,0.0001248233,0.00003715281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001048116,0.0002209897,0.00441497,0.0003654661,0.00021852,0.0003464495,0.0002830919,0.1381728,0.0672958,0.08243728,0.06770077,0.6374958],"study_design_scores_gemma":[0.00006663042,0.00008909582,0.0004783328,0.0000216107,0.00001903059,0.000142172,0.00002775427,0.9187129,0.04428051,0.0157399,0.02039567,0.000026459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03417426,0.0006605444,0.9232315,0.0002361406,0.0001441702,0.00006055172,0.0002848655,0.03197937,0.009228581],"genre_scores_gemma":[0.2891856,0.0003509247,0.6893657,0.0004165396,0.00006955122,0.0002602763,0.001829601,0.004263136,0.01425857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00620874,"threshold_uncertainty_score":0.01665878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008153065125548538,"score_gpt":0.2143165338795878,"score_spread":0.2061634687540393,"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."}}