{"id":"W2077587664","doi":"10.1016/j.vlsi.2007.03.001","title":"Gate-level dual-threshold static power optimization methodology (GDSPOM) using path-based static timing analysis (STA) technique for SOC application","year":2007,"lang":"en","type":"article","venue":"Integration","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Threshold voltage; Leakage power; Standby power; Static timing analysis; CMOS; Electronic engineering; Power (physics); Multiplier (economics); Low-power electronics; Power consumption; Power analysis; Computer science; Dual (grammatical number); Path (computing); Power optimization; Voltage; Engineering; Electrical engineering; Transistor; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001766337,0.0003035547,0.0003883267,0.0008751993,0.0001402617,0.00006985386,0.0001320474,0.0002464037,0.00005316854],"category_scores_gemma":[0.0001473854,0.0003058979,0.0001431353,0.00119244,0.00004800961,0.0004100473,0.00001188141,0.0002123517,0.000007991327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004793226,"about_ca_system_score_gemma":0.00007030307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005083009,"about_ca_topic_score_gemma":0.00007203793,"domain_scores_codex":[0.9981404,0.00008229431,0.0007127831,0.0003647198,0.0002736778,0.0004261284],"domain_scores_gemma":[0.9985923,0.0004327827,0.0002086493,0.0003821719,0.000301263,0.00008285487],"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":[0.00004568757,0.00003096194,0.0001365276,0.00006650727,0.0001169158,0.000001808525,0.0004268407,0.8353314,0.1607419,0.000400734,0.00008800792,0.002612739],"study_design_scores_gemma":[0.0003129832,0.00007308003,0.0002172417,0.00003700913,0.0002866352,0.00000271783,0.0002239407,0.831791,0.1663602,0.0003602078,0.00005272621,0.0002822473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0366234,0.00003475858,0.9613802,0.00004492775,0.0002037296,0.00123393,0.00004581315,0.0003367088,0.00009653984],"genre_scores_gemma":[0.5464975,0.000004896463,0.452896,0.0000588939,0.00003119929,0.0001961467,0.0002542362,0.00004996802,0.0000111968],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.509874,"threshold_uncertainty_score":0.9999393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07817558374840947,"score_gpt":0.324965834286492,"score_spread":0.2467902505380825,"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."}}