{"id":"W4386361363","doi":"10.1109/nano58406.2023.10231157","title":"A Variable Latency Ling Adder Based on Brent-Kung Parallel-Prefix Topology","year":2023,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Adder; Power–delay product; Computer science; Latency (audio); Compiler; Parallel computing; Arithmetic; Serial binary adder; Standard cell; Carry-save adder; Algorithm; Mathematics; Integrated circuit; Telecommunications","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002151394,0.0002186711,0.0002195884,0.0002707605,0.00008530424,0.00003859959,0.0002176942,0.0001601623,0.001647239],"category_scores_gemma":[0.00002719717,0.000199035,0.00005528766,0.0006353409,0.00002376456,0.0001545993,0.00003583257,0.0002345299,0.002279791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006702018,"about_ca_system_score_gemma":0.00003591032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004262375,"about_ca_topic_score_gemma":0.000008840292,"domain_scores_codex":[0.9986982,0.00002607293,0.0002511106,0.0002600409,0.0001877229,0.0005768408],"domain_scores_gemma":[0.999333,0.000132002,0.00001953301,0.0003913563,0.00002848089,0.00009563843],"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":[0.00002214927,0.00003653668,0.001154896,0.00009581436,0.00005683189,0.00003504917,0.0001618948,0.947287,0.004649472,0.005121159,0.04003659,0.001342612],"study_design_scores_gemma":[0.0006956399,0.00008857188,0.001259943,0.00004855233,0.00001701558,0.000003232197,0.00003312955,0.981148,0.003189834,0.0005657675,0.01260273,0.000347578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.223754,0.0003694783,0.3680671,0.001455894,0.007526794,0.001215089,0.00003530318,0.01377195,0.3838044],"genre_scores_gemma":[0.9793077,0.00004151998,0.01572564,0.0003900469,0.000192529,0.00006702499,0.00003934682,0.00008795153,0.004148195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7555537,"threshold_uncertainty_score":0.9992654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158266738304772,"score_gpt":0.2117570623978876,"score_spread":0.2001743950148399,"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."}}