{"id":"W2068916399","doi":"10.1109/ccece.2008.4564571","title":"Power aware design of superscalar architecture for high performance DSP operations","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Superscalar; Computer science; Very long instruction word; Digital signal processing; Computer architecture; Power (physics); Microarchitecture; Architecture; Software; Pipeline burst cache; Embedded system; Parallel computing; Computer hardware; Operating system; CPU cache; Cache; Cache algorithms","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002132006,0.0003808525,0.0002526871,0.0003498242,0.0002683613,0.0005065048,0.0008953417,0.0002123916,0.001946593],"category_scores_gemma":[0.0002943515,0.0002069364,0.0003021888,0.0003265634,0.0001618915,0.0004495566,0.0002056822,0.0004067465,0.0003873899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003931713,"about_ca_system_score_gemma":0.0008034728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183085,"about_ca_topic_score_gemma":0.003781394,"domain_scores_codex":[0.9997837,0.00003906093,0.00001277393,0.00003068888,0.0001007345,0.00003316213],"domain_scores_gemma":[0.9998453,0.0000313839,0.00002946452,0.00002222071,0.00006404368,0.000007668926],"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":[0.0001503972,0.0001138154,0.001898402,0.0004855163,0.0001567443,0.0002999503,0.0002200205,0.4758335,0.2340748,0.03190547,0.005436954,0.2494244],"study_design_scores_gemma":[0.00002733871,0.0002470012,0.000776856,0.00002541404,0.00006898645,0.0001681446,0.00005103302,0.9293084,0.05161173,0.005569525,0.01213105,0.00001453215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06282879,0.0007525469,0.9266635,0.0001192808,0.00004813292,0.0001077296,0.00008936058,0.001401364,0.007989362],"genre_scores_gemma":[0.5873038,0.0005527164,0.4077591,0.0001045591,0.00003642293,0.0001648756,0.0002211913,0.0002252901,0.003632101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001946593,"threshold_uncertainty_score":0.006512046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746222034452253,"score_gpt":0.1976937537700633,"score_spread":0.1802315334255408,"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."}}