{"id":"W2165203480","doi":"10.1109/43.851996","title":"Analytical models for RTL power estimation of combinational and sequential circuits","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Intel Corporation","keywords":"Combinational logic; Benchmark (surveying); Sequential logic; Algorithm; Computer science; Quadratic equation; Power (physics); Electronic circuit; Logic gate; Mathematics; Electronic engineering; Engineering; Electrical engineering","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":[0.0004315624,0.0008955816,0.000559815,0.0008082927,0.0002876623,0.0007200419,0.001707759,0.0008413867,0.003563907],"category_scores_gemma":[0.002879735,0.0007084409,0.0007957598,0.0007043006,0.0003931104,0.001871848,0.0003331463,0.0009274243,0.001439831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105541,"about_ca_system_score_gemma":0.0006308745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002823853,"about_ca_topic_score_gemma":0.003682644,"domain_scores_codex":[0.9995959,0.00006539848,0.00002065196,0.00007065989,0.0002064023,0.00004097546],"domain_scores_gemma":[0.9992334,0.0003659415,0.0001302024,0.0001055158,0.0001541203,0.00001086536],"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":[0.00001642569,0.00001954821,0.0002042958,0.00006071154,0.0000171043,0.00003270541,0.00004924745,0.9688125,0.007063649,0.00582458,0.0003803186,0.01751906],"study_design_scores_gemma":[0.00000127557,0.000007591993,0.00003646385,0.00000292084,0.000003371511,0.00001156333,0.000002350248,0.9970191,0.001064749,0.001316484,0.0005317532,0.000002478397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008573979,0.0002207812,0.9880447,0.00008317185,0.00001241959,0.00003321353,0.0001027488,0.0008193345,0.002109746],"genre_scores_gemma":[0.7781237,0.001108702,0.2085984,0.0001579137,0.00007271505,0.0004183313,0.0004909761,0.0005707924,0.01045845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003563907,"threshold_uncertainty_score":0.01192248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882853325909728,"score_gpt":0.2290329034610601,"score_spread":0.2002043702019628,"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."}}