{"id":"W2954748826","doi":"10.1109/tc.2019.2926275","title":"Design and Analysis of Area and Power Efficient Approximate Booth Multipliers","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Multiplier (economics); Booth's multiplication algorithm; Computer science; Arithmetic; Algorithm; Approximation error; Adder; Mathematics","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":[0.0002211653,0.0003695597,0.0003139006,0.0003290225,0.0002434013,0.000595698,0.0004818086,0.0002638398,0.001921182],"category_scores_gemma":[0.0009892187,0.0002184367,0.0002186278,0.0003980653,0.0002201221,0.0006031553,0.000207708,0.0002193768,0.000292018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005796683,"about_ca_system_score_gemma":0.0007210479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007773156,"about_ca_topic_score_gemma":0.001261886,"domain_scores_codex":[0.9997678,0.00004521691,0.000009538098,0.00002861848,0.0001207394,0.00002804165],"domain_scores_gemma":[0.9997029,0.0001040695,0.00007022607,0.00003759232,0.00007672197,0.00000854665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005062905,0.00008353271,0.003168609,0.0004030529,0.00009461922,0.0002927978,0.0002125429,0.5482244,0.1314053,0.07324499,0.003217759,0.239146],"study_design_scores_gemma":[0.00004685173,0.0005536479,0.0007505991,0.00002204661,0.00002869645,0.0002561146,0.00004032302,0.9589774,0.02576901,0.005728899,0.007813289,0.00001305319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.288409,0.001871792,0.6925512,0.0003345642,0.00006636726,0.0001687504,0.0001383479,0.0006057374,0.01585425],"genre_scores_gemma":[0.854843,0.000527646,0.1395743,0.00005660888,0.0000322509,0.0001170585,0.00008883891,0.00002627561,0.004733988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001921182,"threshold_uncertainty_score":0.00642699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008221479260659581,"score_gpt":0.1870785827645566,"score_spread":0.178857103503897,"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."}}