{"id":"W3200906050","doi":"10.1109/mwscas47672.2021.9531923","title":"Hearing aid and Extreme Edge IoT Acceleration","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"ON Semiconductor (Canada)","funders":"","keywords":"Computer science; Computation; Enhanced Data Rates for GSM Evolution; Edge computing; Cloud computing; Benchmark (surveying); Software deployment; Edge device; Distributed computing; Field (mathematics); Key (lock); Acceleration; Computer engineering; Artificial intelligence; Computer security; Algorithm; Software engineering; Operating system","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":[0.0002605876,0.0006320986,0.0003669978,0.0004846026,0.0002849387,0.0007590887,0.001079793,0.0004649375,0.01176112],"category_scores_gemma":[0.0009892056,0.0001039228,0.00027622,0.000790784,0.000353641,0.0006453263,0.0008404384,0.0005158023,0.002219107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003703357,"about_ca_system_score_gemma":0.0003134063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001924465,"about_ca_topic_score_gemma":0.001796064,"domain_scores_codex":[0.9995862,0.00004066287,0.00001429894,0.00004720296,0.0002199315,0.00009166017],"domain_scores_gemma":[0.9997761,0.00006787601,0.00001799403,0.00004250255,0.00006606728,0.00002954064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004916717,0.0006208799,0.008865652,0.001500953,0.000124723,0.002212808,0.0003493032,0.210679,0.1603791,0.03316966,0.05481461,0.5223666],"study_design_scores_gemma":[0.0003833096,0.004251758,0.02372502,0.0002702108,0.0001435886,0.004036362,0.0006218971,0.5831287,0.1877033,0.02656862,0.1689771,0.0001902347],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5673527,0.003540819,0.2286084,0.001226409,0.001105602,0.0004227385,0.002909378,0.013814,0.18102],"genre_scores_gemma":[0.9427468,0.0004316112,0.04152991,0.0001532767,0.00003937365,0.00009256502,0.001150799,0.0002559068,0.01359985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01176112,"threshold_uncertainty_score":0.03934491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742651141751438,"score_gpt":0.216025982491641,"score_spread":0.1785994710741266,"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."}}