{"id":"W1969775274","doi":"10.1109/tvlsi.2007.893584","title":"Segmented Virtual Ground Architecture for Low-Power Embedded SRAM","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Static random-access memory; Dynamic demand; Computer science; Dissipation; Low-power electronics; Electronic engineering; Dynamic voltage scaling; Power consumption; Power (physics); Soft error; Energy consumption; Embedded system; Engineering; Electrical engineering; Computer hardware","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":[0.00006333365,0.0002205881,0.0001359754,0.0002143984,0.0001749482,0.0002912345,0.0007194545,0.000170462,0.001545645],"category_scores_gemma":[0.000128227,0.00009143905,0.0001208773,0.0002823586,0.0002018723,0.0005202085,0.0002462415,0.0001789882,0.0002409912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002570626,"about_ca_system_score_gemma":0.0002357718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003468732,"about_ca_topic_score_gemma":0.00115537,"domain_scores_codex":[0.9999442,0.0000102285,0.000004288711,0.00001042961,0.00002064416,0.00001020714],"domain_scores_gemma":[0.9999063,0.00001797854,0.00001636635,0.0000269439,0.000024485,0.000007957055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004694807,0.00009102218,0.001176577,0.000242246,0.00007672721,0.0004977447,0.0001499793,0.04138887,0.4849982,0.03219505,0.006017126,0.4326969],"study_design_scores_gemma":[0.0002106392,0.003237628,0.00296038,0.00005675915,0.0002127875,0.001734239,0.0001089972,0.4779241,0.406337,0.03670171,0.07045107,0.00006462631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3734647,0.003245139,0.6091831,0.0002863992,0.0002231018,0.00005559416,0.0001599519,0.004114306,0.009267673],"genre_scores_gemma":[0.8978556,0.0004002663,0.09722712,0.0001242197,0.00004295478,0.00003460036,0.0001796827,0.00005425914,0.004081252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001545645,"threshold_uncertainty_score":0.005170703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006939423437098234,"score_gpt":0.2207381096543361,"score_spread":0.2137986862172379,"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."}}