{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006848051,0.0001401479,0.0001035151,0.0001308413,0.0007432352,0.0000486157,0.0004212811,0.00002926823,0.00004154598],"category_scores_gemma":[0.000004662963,0.0001288262,0.0000324936,0.0005838963,0.00005156457,0.0001097073,0.00003690513,0.0003247322,0.000001756562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005715804,"about_ca_system_score_gemma":0.00003598354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007132769,"about_ca_topic_score_gemma":0.00003118024,"domain_scores_codex":[0.9989745,0.00007806705,0.0001398209,0.000406871,0.0002176388,0.0001831234],"domain_scores_gemma":[0.9991312,0.00009793878,0.00007093562,0.0005887817,0.00003773643,0.00007340991],"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":[0.00003336849,0.00004843162,0.000005569727,0.000003399102,0.00001103716,0.000001482661,0.0002620449,0.8975719,0.0001521198,0.001277749,0.000007050017,0.1006259],"study_design_scores_gemma":[0.00108233,0.0006712477,0.0002243799,0.00001721137,0.00003930766,0.0002237329,0.0001225882,0.9782432,0.002164911,0.009640361,0.007103852,0.0004668335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001695906,0.00004307334,0.9932058,0.004223312,0.00006933871,0.0003436398,0.00001510612,0.0002214732,0.0001823095],"genre_scores_gemma":[0.6355888,0.00004429944,0.363258,0.0005784552,0.00001669998,0.0003050484,0.00001678327,0.00001676852,0.0001751092],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.633893,"threshold_uncertainty_score":0.5716439,"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."}}