{"id":"W2153585893","doi":"10.1145/611817.611838","title":"Using logic duplication to improve performance in FPGAs","year":2003,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Path (computing); Computer science; Critical path method; Programmable logic device; Logic synthesis; Logic optimization; Logic gate; Parallel computing; Gene duplication; Data deduplication; Algorithm; Embedded system; Engineering; Programming language","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":[0.00008352053,0.00005575072,0.00005500996,0.00006776468,0.00001156629,0.000009290374,0.0000431297,0.00003747981,0.00003575225],"category_scores_gemma":[0.000007823649,0.00005282044,0.000009395108,0.0001489928,0.000003064186,0.00007597648,0.000004590665,0.00004880574,0.00003486823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005566665,"about_ca_system_score_gemma":0.000004302367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007353409,"about_ca_topic_score_gemma":0.000003634932,"domain_scores_codex":[0.9996778,0.000004745645,0.00008921969,0.00007514369,0.00003526148,0.0001177778],"domain_scores_gemma":[0.9998465,0.000004724486,0.000004937427,0.00010926,0.000009467733,0.00002511168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002889224,0.00002919781,0.005448648,0.0000727355,0.000005611234,0.000002170171,0.0001979168,0.03585773,0.9094557,0.01344023,0.000471199,0.03501596],"study_design_scores_gemma":[0.0001312334,0.00005429595,0.003453104,0.00002178446,0.000002702225,0.000006368708,0.00002528055,0.1737695,0.8171729,0.001102002,0.004007265,0.0002535243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4931749,0.0000478507,0.4512253,0.00001407653,0.00006537765,0.0002204679,3.62714e-7,0.0003963777,0.05485535],"genre_scores_gemma":[0.9667802,0.00001572174,0.03299743,0.00006436554,0.000009791582,0.00001980518,3.635708e-7,0.000009689059,0.0001026758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4736053,"threshold_uncertainty_score":0.2153955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02547936727386112,"score_gpt":0.2480734802009621,"score_spread":0.222594112927101,"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."}}