{"id":"W2153007772","doi":"10.1109/isqed.2011.5770732","title":"Enhancement of incremental design for FPGAs using circuit similarity","year":2011,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Netlist; Similarity (geometry); Computer science; Circuit extraction; Matching (statistics); Design flow; Algorithm; Physical design; Field-programmable gate array; Process (computing); Plug-in; Feature (linguistics); Logic synthesis; Electronic circuit; Computer engineering; Theoretical computer science; Circuit design; Logic gate; Artificial intelligence; Computer hardware; Equivalent circuit; Embedded system; Mathematics; Engineering; Programming language; Image (mathematics)","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":[0.0004084278,0.0006133927,0.0003605149,0.001149356,0.0003010336,0.0004581366,0.0009490621,0.0002709259,0.0023847],"category_scores_gemma":[0.00136573,0.000268875,0.0005666853,0.0005313929,0.0003345199,0.0007517718,0.0006642811,0.0003351999,0.0004479845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004810604,"about_ca_system_score_gemma":0.0005458247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008455677,"about_ca_topic_score_gemma":0.001613179,"domain_scores_codex":[0.9995518,0.00008354084,0.00003038522,0.00005821665,0.000240189,0.00003579885],"domain_scores_gemma":[0.9993849,0.0002279696,0.00008171401,0.0001303835,0.0001547812,0.00002022658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000172049,0.00006981376,0.001686619,0.0002565525,0.00005263019,0.000264483,0.0001869268,0.08988243,0.09918816,0.02242798,0.001941648,0.7838706],"study_design_scores_gemma":[0.00009316298,0.0008925374,0.001743281,0.00003884293,0.0001125575,0.00104882,0.00008506647,0.8063939,0.1410644,0.01828246,0.03019449,0.00005044041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02671988,0.0001669975,0.9685174,0.00003691415,0.0000243636,0.00008364043,0.00002010508,0.001759098,0.002671614],"genre_scores_gemma":[0.2875731,0.0001312185,0.7099971,0.00005579754,0.00002338626,0.0000840727,0.0001344639,0.0001629354,0.001837944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0023847,"threshold_uncertainty_score":0.007977605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1564762563193712,"score_gpt":0.2623575282494788,"score_spread":0.1058812719301076,"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."}}