{"id":"W4408555961","doi":"10.51846/jcsa.v1i2.3932","title":"Hybrid Neuromorphic-Deep Learning Systems for AI Acceleration in Edge Computing","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Science and Applications (JCSA) ISSN 3079-0867 (Onilne)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Neuromorphic engineering; Acceleration; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Edge computing; Deep learning; Computer architecture; Artificial neural network; Physics","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":[0.0002104568,0.000330227,0.0002866449,0.0002088701,0.0002235519,0.0005931586,0.0009631183,0.0004224102,0.003754033],"category_scores_gemma":[0.0004518394,0.0001292656,0.0002319313,0.0002621596,0.000255722,0.0009465149,0.0007710522,0.0005024978,0.0005919788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004114351,"about_ca_system_score_gemma":0.0003795569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008738568,"about_ca_topic_score_gemma":0.00229702,"domain_scores_codex":[0.9999002,0.00001503379,0.000006167515,0.00002173273,0.00003765388,0.00001911897],"domain_scores_gemma":[0.999887,0.00003135305,0.00001369165,0.00001891409,0.0000358576,0.00001312764],"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":[0.0003622239,0.0003750804,0.002124621,0.0004905636,0.0001512369,0.0003912975,0.0001496824,0.3908353,0.1313244,0.04720182,0.00697873,0.4196151],"study_design_scores_gemma":[0.00001158264,0.0001130885,0.0003043561,0.00002262239,0.00002200067,0.00009113432,0.00002095242,0.9667277,0.01744856,0.009984655,0.0052421,0.00001124528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09635344,0.001204543,0.8816612,0.0005139646,0.0001956752,0.00008192928,0.0001664152,0.002488868,0.01733398],"genre_scores_gemma":[0.8655984,0.0004581675,0.1266162,0.0003090134,0.00003340499,0.00008690515,0.0001250366,0.00007198763,0.006700857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003754033,"threshold_uncertainty_score":0.01255846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393529523343403,"score_gpt":0.290722473664146,"score_spread":0.266787178430712,"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."}}