{"id":"W4287734027","doi":"10.1109/tcsi.2022.3167894","title":"An Energy-Efficient Approximate Divider Based on Logarithmic Conversion and Piecewise Constant Approximation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Logarithm; Piecewise; Constant (computer programming); Mathematics; Approximation error; Energy (signal processing); Discrete mathematics; Divisor (algebraic geometry); Integer (computer science); Algorithm; Computer science; Applied mathematics; Mathematical analysis; Statistics","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":[0.0001835671,0.0004931347,0.0005116838,0.0004766479,0.0004095347,0.0008792405,0.0008512723,0.0004444096,0.002840668],"category_scores_gemma":[0.0006328265,0.0001971598,0.0003522099,0.0006823878,0.0003073892,0.001051839,0.0004561487,0.0004962005,0.0007839738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004575468,"about_ca_system_score_gemma":0.0004065373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008923167,"about_ca_topic_score_gemma":0.001120428,"domain_scores_codex":[0.9998198,0.00002461168,0.00001268834,0.00004318772,0.00007272719,0.00002711116],"domain_scores_gemma":[0.9998599,0.00004017933,0.00002587089,0.00003001848,0.00003595525,0.000008030938],"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":[0.0006695939,0.0001540726,0.001217835,0.0004829163,0.00008270688,0.0004610908,0.0003076783,0.1228902,0.246455,0.08597937,0.005830332,0.5354691],"study_design_scores_gemma":[0.00007960996,0.0004232875,0.0003238484,0.00004340621,0.00008733011,0.0008784089,0.0000644298,0.8387762,0.1254419,0.009572489,0.02426395,0.00004503719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05377077,0.001235353,0.9297543,0.0002495394,0.0001157895,0.00009469208,0.00007508641,0.001639105,0.01306538],"genre_scores_gemma":[0.6724693,0.0007856138,0.3183137,0.0001626875,0.00006814766,0.0001016021,0.0001424577,0.00007866041,0.007877775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002840668,"threshold_uncertainty_score":0.009503007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008881521860746356,"score_gpt":0.1813336661585219,"score_spread":0.1724521442977755,"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."}}