{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005569626,0.001265384,0.0007033766,0.001589329,0.0005081548,0.001363878,0.003018338,0.0008062615,0.01896262],"category_scores_gemma":[0.001197865,0.0007352666,0.000610739,0.002138493,0.0002597787,0.003511675,0.001102795,0.001048438,0.004479211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006404,"about_ca_system_score_gemma":0.001084525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001714115,"about_ca_topic_score_gemma":0.002243371,"domain_scores_codex":[0.9996089,0.00004555301,0.00004414127,0.00007178685,0.0001741486,0.00005551957],"domain_scores_gemma":[0.9995013,0.0001520129,0.00005155344,0.00006259014,0.0001993461,0.00003306054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006351541,0.0001503931,0.002068709,0.006283586,0.0001695798,0.0002689944,0.0002586124,0.01107982,0.03289134,0.04759845,0.05534461,0.8432509],"study_design_scores_gemma":[0.00008460463,0.0009492422,0.001379727,0.001629445,0.0003479068,0.0009411261,0.0002002044,0.05451501,0.07328788,0.01304082,0.8535144,0.0001096936],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.06805567,0.4519905,0.3437717,0.002449498,0.002061976,0.0005222179,0.002325275,0.008913347,0.1199097],"genre_scores_gemma":[0.2989106,0.2694194,0.3298742,0.002879599,0.00113003,0.001057799,0.006271241,0.001799435,0.08865771],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01896262,"threshold_uncertainty_score":0.06343627,"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."}}