{"id":"W7125508699","doi":"10.1109/icces67310.2025.11336863","title":"Low-Power VLSI Accelerators for Edge AI in IoT Devices","year":2025,"lang":"","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Very-large-scale integration; Edge device; Internet of Things; Key (lock)","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":[0.0001723673,0.0004054638,0.0002464057,0.0003261114,0.0003305014,0.0008279178,0.001164612,0.00041056,0.0218893],"category_scores_gemma":[0.0006602011,0.0001942359,0.0002068425,0.0004614402,0.0001863112,0.001273096,0.0004990681,0.0008965612,0.003777699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004790675,"about_ca_system_score_gemma":0.0005467221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004524476,"about_ca_topic_score_gemma":0.001993411,"domain_scores_codex":[0.9999026,0.000008251122,0.000004708213,0.00001631708,0.00004819973,0.00001999335],"domain_scores_gemma":[0.9997625,0.00006137257,0.00002966739,0.00002723794,0.00008808234,0.00003114648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001124187,0.0005290565,0.004007603,0.001603057,0.000221366,0.0007236016,0.0004772151,0.01423189,0.444781,0.1325961,0.08119763,0.3185073],"study_design_scores_gemma":[0.0002192222,0.002129051,0.004000394,0.0003211212,0.0002300595,0.001125653,0.0004130354,0.3360206,0.3812284,0.04695814,0.2272345,0.0001197529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1793917,0.008383619,0.6241693,0.004155375,0.002537736,0.0004265321,0.001257277,0.008483542,0.1711949],"genre_scores_gemma":[0.787434,0.001479508,0.1487979,0.001251488,0.000307078,0.0001504649,0.0008491632,0.0003675649,0.05936287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0218893,"threshold_uncertainty_score":0.07322699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008557982932482998,"score_gpt":0.251674090707579,"score_spread":0.243116107775096,"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."}}