{"id":"W2143699720","doi":"10.1109/82.868458","title":"A fast parallel multiplier-accumulator using the modified Booth algorithm","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Datapath; Booth's multiplication algorithm; Computer science; Speedup; Multiplier (economics); Encoder; Accumulator (cryptography); Parallel computing; Algorithm; Computer hardware; 16-bit; Carry (investment); Adder","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":[0.0001474517,0.0004210187,0.0003209689,0.000709829,0.0004007119,0.0003888076,0.000562623,0.0002813731,0.003336273],"category_scores_gemma":[0.000439555,0.0002388733,0.0002815274,0.0006437868,0.000263345,0.0008283269,0.0003138288,0.000322938,0.0008618852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003715288,"about_ca_system_score_gemma":0.0006781123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512415,"about_ca_topic_score_gemma":0.002375023,"domain_scores_codex":[0.9998666,0.00002312858,0.000009026638,0.00003172402,0.00005228072,0.0000171822],"domain_scores_gemma":[0.9998358,0.00005489751,0.00002164462,0.00003736291,0.00004118709,0.000008874469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006248653,0.0001089171,0.001362138,0.0003885848,0.00009524037,0.0004381492,0.0001838639,0.1044462,0.1774645,0.07957957,0.0062215,0.6290865],"study_design_scores_gemma":[0.0001460621,0.0006338897,0.001217393,0.00005274775,0.00008384594,0.001386185,0.00004110542,0.7634021,0.13404,0.03626382,0.06263088,0.0001019565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0377478,0.000639955,0.951378,0.0001128204,0.00007386267,0.0001061195,0.0001385475,0.003908071,0.005894718],"genre_scores_gemma":[0.2820148,0.0003027701,0.7121249,0.00007631275,0.0000514651,0.00009938424,0.0001878913,0.0001842235,0.004958338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003336273,"threshold_uncertainty_score":0.01116097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234280248077349,"score_gpt":0.2208206817608095,"score_spread":0.198477879280036,"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."}}