{"id":"W2103117726","doi":"10.1109/tvlsi.2005.859561","title":"Routing architecture optimizations for high-density embedded programmable IP cores","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Routing (electronic design automation); Logic block; Block (permutation group theory); Programmable logic array; Programmable logic device; Parallel computing; Channel (broadcasting); Computer science; Programmable Array Logic; Gate array; Logic gate; Simple programmable logic device; Block size; Square (algebra); Field-programmable gate array; Topology (electrical circuits); Logic synthesis; Computer hardware; Logic family; Embedded system; Engineering; Algorithm; Mathematics; Electrical engineering; Telecommunications; Key (lock); Geometry","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":[0.0002006149,0.0004126004,0.0002070864,0.0003802619,0.0001961806,0.0003375616,0.0004108885,0.0002145489,0.001253146],"category_scores_gemma":[0.0007433422,0.0002098485,0.000181068,0.0004324682,0.0001604392,0.0005910328,0.0002454259,0.0002113434,0.0002218583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004278824,"about_ca_system_score_gemma":0.000415537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008025186,"about_ca_topic_score_gemma":0.003247733,"domain_scores_codex":[0.9998589,0.00003181949,0.00001013927,0.00002101217,0.00004090694,0.00003724345],"domain_scores_gemma":[0.9996848,0.0001008937,0.00007020203,0.00005074109,0.00007889993,0.00001453994],"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":[0.0003505086,0.0001354212,0.003174335,0.0002269282,0.00007837502,0.0002678569,0.00007186686,0.6282482,0.2225695,0.008716216,0.002950324,0.1332104],"study_design_scores_gemma":[0.00008039805,0.0005218548,0.003893488,0.00001648782,0.00007625813,0.0003037045,0.00008128605,0.9159831,0.06986503,0.004057586,0.005096553,0.00002425475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644217,0.0009211144,0.1229758,0.0002127646,0.00003313595,0.0000586037,0.0001160784,0.001054089,0.01020671],"genre_scores_gemma":[0.9499591,0.0002205342,0.04812471,0.00004479019,0.00001068872,0.00002758399,0.0001464091,0.00008672279,0.001379561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001253146,"threshold_uncertainty_score":0.004192233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009668095962273072,"score_gpt":0.2233624758664996,"score_spread":0.2136943799042265,"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."}}