{"id":"W2118828191","doi":"10.1109/cicc.1991.164141","title":"Optimization of field-programmable gate array logic block architecture for speed","year":2002,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multiplexer; Programmable Array Logic; Programmable logic array; Logic block; Field-programmable gate array; Complex programmable logic device; Macrocell array; Programmable logic device; Simple programmable logic device; Routing (electronic design automation); Computer science; Block (permutation group theory); Logic synthesis; Logic gate; Gate array; Erasable programmable logic device; Logic family; Logic optimization; Set (abstract data type); Computer hardware; Algorithm; Embedded system; Multiplexing; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006552576,0.0001364956,0.0001657193,0.00008888426,0.00003135625,0.00001994525,0.0001311074,0.00009315808,0.0005126297],"category_scores_gemma":[0.00002012071,0.0001160103,0.00006294381,0.0001928797,0.00001447537,0.00008580049,0.000008129673,0.00008868979,0.00002769009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001753204,"about_ca_system_score_gemma":0.000002971865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004655097,"about_ca_topic_score_gemma":0.000004520065,"domain_scores_codex":[0.9993026,0.000006705556,0.0002047929,0.0001281893,0.0001007873,0.0002569411],"domain_scores_gemma":[0.9996215,0.00005282151,0.00002912143,0.0002073584,0.00004262785,0.00004664217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000758364,0.00002052702,0.00003442206,0.00009771398,0.0000258145,5.953431e-7,0.0001496632,0.9868497,0.005819943,0.00009213435,0.003690724,0.003211134],"study_design_scores_gemma":[0.0005399645,0.0001806823,0.000008856484,0.00002060506,0.00001985467,0.000005211415,0.0000192172,0.8797151,0.1132378,0.0001847471,0.005863181,0.0002048015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01729354,0.0002358467,0.9595615,0.0002427015,0.0003332715,0.0005943988,0.000005314935,0.0005176691,0.0212157],"genre_scores_gemma":[0.823447,0.00008838603,0.1744476,0.0001101806,0.0001045524,0.00002673656,0.000009676191,0.00004453617,0.001721325],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8061535,"threshold_uncertainty_score":0.5612936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405849072536627,"score_gpt":0.2014109054563562,"score_spread":0.1873524147309899,"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."}}