{"id":"W4385080174","doi":"10.1109/iscas46773.2023.10182097","title":"SEVDA: Singular Value Decomposition Based Parallel Write Scheme for Memristive CNN Accelerators","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Computer science; Singular value decomposition; Parallel computing; Convolutional neural network; FLOPS; Artificial neural network; Von Neumann architecture; Multiplication (music); Matrix multiplication; Algorithm; Artificial intelligence; Mathematics; Quantum","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":[0.000114294,0.0001482835,0.000151371,0.00008575134,0.0001470178,0.00002793272,0.00008488816,0.0000579501,0.00002399973],"category_scores_gemma":[0.00002729348,0.0001504374,0.00007802302,0.000264117,0.00001268227,0.0001408991,0.00002115283,0.0001051031,0.00005301748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004508076,"about_ca_system_score_gemma":0.000009004171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.071334e-7,"about_ca_topic_score_gemma":7.017496e-7,"domain_scores_codex":[0.9992445,0.00001335559,0.0001767709,0.0001918464,0.00008740791,0.0002861815],"domain_scores_gemma":[0.9995769,0.0001767391,0.00002134036,0.0001216755,0.00003671695,0.0000666878],"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":[0.00003661069,0.00001350427,0.00007230645,0.0001083173,0.00003206192,0.00001560473,0.00006933983,0.8638889,0.1295073,0.001258102,0.00171194,0.003285924],"study_design_scores_gemma":[0.0005083613,0.0000394753,0.0003630901,0.00003405847,0.00001006389,0.000001489244,0.00003572658,0.8378708,0.158534,0.000914877,0.001463339,0.0002246853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3866838,0.00004530217,0.6104841,0.00007807746,0.0002893118,0.0002716412,0.000007932547,0.001302858,0.0008369045],"genre_scores_gemma":[0.9414911,0.000005959529,0.05783293,0.0001941866,0.0001562843,0.00003626426,0.00008179282,0.00004532401,0.0001561105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5548073,"threshold_uncertainty_score":0.6134659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645811150862435,"score_gpt":0.2923820822869485,"score_spread":0.2659239707783241,"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."}}