{"id":"W4229447288","doi":"10.3390/fi14050146","title":"A Survey on Memory Subsystems for Deep Neural Network Accelerators","year":2022,"lang":"en","type":"article","venue":"Future Internet","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial neural network; Computer architecture; Application-specific integrated circuit; Memory map; In-Memory Processing; Computation; Deep learning; Artificial intelligence; Computer engineering; Embedded system; Semiconductor memory; Computer hardware; Programming language","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":[0.0002385409,0.000170699,0.0001890371,0.00004041633,0.0001240869,0.00002749217,0.0002549154,0.00003974113,0.0001035799],"category_scores_gemma":[0.00001498097,0.0001687688,0.00008061693,0.0001639642,0.000007275744,0.00005692039,0.00007385961,0.0003526845,0.000009706784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000756897,"about_ca_system_score_gemma":0.000003822272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007927096,"about_ca_topic_score_gemma":0.0000781724,"domain_scores_codex":[0.9990597,0.00008808887,0.0002022407,0.0002095712,0.0001237438,0.0003166482],"domain_scores_gemma":[0.9995211,0.0001847921,0.00004103683,0.000174553,0.00001941679,0.00005904617],"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":[0.0001579836,0.00001507404,0.0005508483,0.00004778492,0.00004250778,0.00002234254,0.0003844584,0.929496,0.0003652801,0.000151585,0.05772887,0.01103727],"study_design_scores_gemma":[0.001478068,0.0009041322,0.009952039,0.00005170975,0.00002635676,0.00006962427,0.0005335326,0.7686381,0.007915331,0.0001607511,0.209178,0.001092286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819197,0.0009042598,0.003476129,0.00004274322,0.01207777,0.0004283572,0.0000558123,0.000464564,0.000630649],"genre_scores_gemma":[0.9966896,0.00000214742,0.00009497701,0.0003135067,0.002313694,0.0000759891,0.00008637406,0.00005153585,0.0003721718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1608578,"threshold_uncertainty_score":0.6882192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223901424306333,"score_gpt":0.2382716479618115,"score_spread":0.2160326337187482,"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."}}