{"id":"W2161942462","doi":"10.1109/tcad.2008.915545","title":"Scalable Synthesis and Clustering Techniques Using Decision Diagrams","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speedup; Binary decision diagram; Computer science; Scalability; Field-programmable gate array; Cluster analysis; Leverage (statistics); Logic synthesis; Electronic design automation; Parallel computing; Reduction (mathematics); Data-flow analysis; Design flow; Theoretical computer science; Algorithm; Data flow diagram; Logic gate; Mathematics; Computer hardware; Embedded system","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":[0.0007245361,0.0009333698,0.0006815435,0.001439324,0.0004992716,0.0008965224,0.0008956276,0.0005165745,0.004817814],"category_scores_gemma":[0.001580428,0.0005865853,0.0009343032,0.001016084,0.0005294835,0.001099396,0.0008276666,0.0008292162,0.0008694151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009290113,"about_ca_system_score_gemma":0.001182904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174186,"about_ca_topic_score_gemma":0.003487485,"domain_scores_codex":[0.9991444,0.000160088,0.00006502814,0.0001901756,0.000377076,0.00006310045],"domain_scores_gemma":[0.9992616,0.0003917901,0.00006196849,0.0001412335,0.0001243393,0.00001913737],"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":[0.0001134276,0.00006206684,0.000435151,0.0004302313,0.00008193746,0.0001419449,0.0001265852,0.4247074,0.04639115,0.06865758,0.002875864,0.4559768],"study_design_scores_gemma":[0.00008424383,0.00007839078,0.0001679577,0.00004273686,0.00004592588,0.0001153449,0.00003606297,0.9157491,0.03077777,0.03886365,0.01401822,0.00002065675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004369265,0.0002023535,0.992888,0.00004967112,0.00001746325,0.00005494371,0.00004975788,0.0007918907,0.001576707],"genre_scores_gemma":[0.08975969,0.0003662856,0.9077392,0.00004708008,0.00001956451,0.0001267992,0.0002772365,0.000138706,0.001525316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004817814,"threshold_uncertainty_score":0.01611722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03977928161711857,"score_gpt":0.2276952282445311,"score_spread":0.1879159466274125,"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."}}