{"id":"W4244472522","doi":"10.1145/384285.379265","title":"Power and energy reduction via pipeline balancing","year":2001,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"PQ Corporation (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Queue; Reduction (mathematics); Power (physics); Embedded system; Energy (signal processing); Dissipation; Chip; Efficient energy use; Power budget; Component (thermodynamics); Real-time computing; Operating system; Electric power system; Engineering; Computer network; 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":[0.0001837543,0.0004342179,0.0002309137,0.000372948,0.0002692129,0.0003563197,0.0007335728,0.0002086808,0.003078392],"category_scores_gemma":[0.0005169799,0.0001890437,0.0001719717,0.0005588884,0.0002286196,0.0009373548,0.000444918,0.000320815,0.0005753466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003200483,"about_ca_system_score_gemma":0.0004911178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006860711,"about_ca_topic_score_gemma":0.001497281,"domain_scores_codex":[0.9998456,0.00001940288,0.000007956844,0.00002618216,0.0000650301,0.00003572519],"domain_scores_gemma":[0.9998253,0.0000425972,0.0000387227,0.00004089803,0.00004031738,0.00001219909],"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":[0.0003430313,0.000173896,0.002092465,0.0002177458,0.00003800012,0.0001443758,0.0001149494,0.07123309,0.5571106,0.01921146,0.004661641,0.3446587],"study_design_scores_gemma":[0.0001318517,0.000789601,0.003355089,0.00002782989,0.00007764706,0.0004118137,0.00007631972,0.6784432,0.2544771,0.03584946,0.02631877,0.00004137931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3022612,0.001345155,0.6779853,0.0006095789,0.00009049157,0.00008080689,0.0001221967,0.003822961,0.01368226],"genre_scores_gemma":[0.878648,0.0004523527,0.1124929,0.0001440248,0.00004535396,0.00006376541,0.000152618,0.000334734,0.00766636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003078392,"threshold_uncertainty_score":0.01029825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007678002474206413,"score_gpt":0.2342863465951199,"score_spread":0.2266083441209135,"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."}}