{"id":"W3107464732","doi":"10.1049/iet-cds.2019.0398","title":"Design, evaluation and application of approximate‐truncated Booth multipliers","year":2020,"lang":"en","type":"article","venue":"IET Circuits Devices & Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Multiplier (economics); Adder; Encoder; Computer science; Cluster analysis; Arithmetic; Algorithm; Approximation error; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003888099,0.0003819274,0.0002582139,0.0003599589,0.0002027085,0.0004900809,0.0005651327,0.0002529125,0.001837355],"category_scores_gemma":[0.00156476,0.0001334844,0.0001447098,0.0003573382,0.0001909735,0.0005415701,0.0002213214,0.0002100168,0.0002656051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003533126,"about_ca_system_score_gemma":0.0005387689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005059215,"about_ca_topic_score_gemma":0.0006565409,"domain_scores_codex":[0.9996759,0.00008121309,0.00002092652,0.00003358591,0.000161004,0.00002735647],"domain_scores_gemma":[0.9993777,0.0001830736,0.00009664486,0.00008107968,0.0002420216,0.00001960135],"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":[0.001011735,0.0001286145,0.003430108,0.0005879126,0.0001117373,0.0003467405,0.000204839,0.3202429,0.1578618,0.02549127,0.002085285,0.4884971],"study_design_scores_gemma":[0.00009278265,0.002004066,0.001378906,0.00005227497,0.00005640404,0.0005865353,0.00006938993,0.8552579,0.1251165,0.003713679,0.01164434,0.00002724906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3961967,0.001996382,0.5900479,0.0002528223,0.0001038178,0.0002220749,0.0001183747,0.0008003624,0.01026159],"genre_scores_gemma":[0.8259366,0.0004756318,0.1700708,0.00004319167,0.00002905243,0.00006702161,0.00008708449,0.00002304197,0.003267631],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001837355,"threshold_uncertainty_score":0.00614655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628194498409745,"score_gpt":0.2311636055124043,"score_spread":0.2048816605283069,"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."}}