{"id":"W1929051941","doi":"10.1109/glsv.1998.665193","title":"Low-power design of finite field multipliers for wireless applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplier (economics); Reduction (mathematics); Computer science; Dynamic demand; Finite field; Power (physics); Logic gate; Low-power electronics; Power optimization; Logic synthesis; Power consumption; Energy consumption; Electronic engineering; Mathematics; Electrical engineering; Algorithm; Engineering","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":[0.0002661387,0.0003893247,0.0002044213,0.0003862016,0.0003098424,0.0005244606,0.0006405138,0.0002543964,0.004075884],"category_scores_gemma":[0.0008314766,0.0001856191,0.0001788283,0.0003342971,0.0001756005,0.0006153478,0.0001512551,0.0003264675,0.000768465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003446368,"about_ca_system_score_gemma":0.000429564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002443542,"about_ca_topic_score_gemma":0.0008787124,"domain_scores_codex":[0.9999142,0.00002343474,0.000005398132,0.00001418082,0.00003200477,0.00001084373],"domain_scores_gemma":[0.9997329,0.0001087301,0.00003843605,0.00001985211,0.00009023723,0.000009795825],"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":[0.0004953329,0.0001688111,0.001044038,0.0005508732,0.00007504413,0.0002608717,0.0002651997,0.1414134,0.2837681,0.09565744,0.004806891,0.4714941],"study_design_scores_gemma":[0.0001792882,0.001367026,0.00099778,0.0001213173,0.00009252899,0.0007152272,0.0001214321,0.7657461,0.1358467,0.05490027,0.0398634,0.00004895308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05732615,0.0007034455,0.9316434,0.0001837131,0.00007594523,0.0001164248,0.00005203626,0.0004599957,0.009438949],"genre_scores_gemma":[0.5810676,0.0008508672,0.4103158,0.0001016646,0.00007866928,0.0001633028,0.0001120138,0.00007885962,0.007231144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004075884,"threshold_uncertainty_score":0.01363522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154035686198542,"score_gpt":0.2028998100270058,"score_spread":0.1874962414071516,"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."}}