{"id":"W3000160544","doi":"10.1109/tvlsi.2019.2961602","title":"Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Korea Evaluation Institute of Industrial Technology","keywords":"STRIDE; Convolutional neural network; Kernel (algebra); Computer science; Speedup; Digital signal processing; Convolution (computer science); Algorithm; Computational complexity theory; Parallel computing; Field-programmable gate array; Mathematics; Artificial neural network; Artificial intelligence; Embedded system; Computer hardware; Discrete mathematics","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":[0.0003529568,0.0008083193,0.0003980184,0.0005810656,0.0004586173,0.001198245,0.001364189,0.0006705642,0.00769902],"category_scores_gemma":[0.001357492,0.0005230019,0.000781614,0.0005732021,0.0004489141,0.001723034,0.00129427,0.001234322,0.003068735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006614343,"about_ca_system_score_gemma":0.001287197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002839821,"about_ca_topic_score_gemma":0.006027062,"domain_scores_codex":[0.9996951,0.00003676602,0.00003074727,0.00007614341,0.0001197587,0.00004152091],"domain_scores_gemma":[0.9996701,0.00005281205,0.00004220889,0.00009322882,0.0001110376,0.00003060013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007155465,0.0001997054,0.003577654,0.0006635349,0.0001745816,0.0005467581,0.0002938059,0.09009975,0.05936175,0.1051914,0.05057132,0.6886042],"study_design_scores_gemma":[0.0001046063,0.0002799687,0.001342938,0.00009868557,0.0000549824,0.001049447,0.0001159916,0.7488597,0.1160178,0.02674665,0.1052079,0.0001212784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03390134,0.0009774528,0.9501387,0.0002417768,0.0003018221,0.0001195175,0.0003526705,0.006593741,0.007372821],"genre_scores_gemma":[0.1302821,0.0006287427,0.8560444,0.000270267,0.00004463787,0.0002572328,0.001208146,0.000989628,0.01027478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00769902,"threshold_uncertainty_score":0.02575576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222694878945663,"score_gpt":0.2494657186137322,"score_spread":0.2271962307191659,"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."}}