{"id":"W3091257769","doi":"10.1109/iscas45731.2020.9180556","title":"RF-Rate Hybrid CNN Accelerator Based on Analog-CMOS and Xilinx RFSoC","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; CMOS; Hardware acceleration; Analogue electronics; Application-specific integrated circuit; Computer hardware; Electronic engineering; Field-programmable gate array; Electronic circuit; Electrical engineering; Engineering","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":[0.00004148711,0.0001276634,0.0001290138,0.00002328073,0.00005609767,0.00002478892,0.0000660435,0.00002290624,0.0001112149],"category_scores_gemma":[0.00003039561,0.0001158658,0.0000279132,0.000100569,0.00001182828,0.00008374279,0.00002107087,0.000158194,0.00004638674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009955918,"about_ca_system_score_gemma":0.00000556525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.284197e-7,"about_ca_topic_score_gemma":6.33087e-7,"domain_scores_codex":[0.9994622,0.00001021446,0.0001188157,0.0001811156,0.0000585455,0.0001690933],"domain_scores_gemma":[0.9996723,0.00008208903,0.00001264521,0.0000942335,0.00001109682,0.0001276259],"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":[0.0001243001,0.00003200643,0.0007329407,0.000286083,0.00004242831,0.000229813,0.000196994,0.5681983,0.3879206,0.000276926,0.006589705,0.03536991],"study_design_scores_gemma":[0.0004208855,0.000106378,0.0003956082,0.00001701025,0.00000683913,0.000002452364,0.00002235735,0.5657029,0.4295041,0.0000557009,0.003541156,0.0002246177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974043,0.00006601598,0.01969665,0.0004065564,0.0001273817,0.0001056232,0.000005250535,0.0005659004,0.004983657],"genre_scores_gemma":[0.9966511,0.000009417704,0.0006692612,0.002454536,0.0001334285,0.000002423569,0.00000404397,0.00002077219,0.00005501423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04158342,"threshold_uncertainty_score":0.4724871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540933168158271,"score_gpt":0.2179255331537475,"score_spread":0.1925162014721648,"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."}}