{"id":"W1540671747","doi":"10.1109/iscas.2015.7168921","title":"Analysis and characterization of data energy tradeoffs: For VLSI architectural agility in C-RAN platforms","year":2015,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Datapath; Very-large-scale integration; Computer science; Computer architecture; Flexibility (engineering); Cloud computing; Adaptation (eye); Efficient energy use; Scaling; Embedded system; Distributed computing; Engineering; Operating system; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003551953,0.0005038168,0.000183312,0.0006444848,0.0002603237,0.0005308671,0.000612766,0.0003629953,0.002502874],"category_scores_gemma":[0.002505725,0.0001438873,0.0002088873,0.0008051946,0.0002987201,0.0008954937,0.0002382149,0.000377341,0.000341148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006072642,"about_ca_system_score_gemma":0.0003770377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001428073,"about_ca_topic_score_gemma":0.003112043,"domain_scores_codex":[0.9996946,0.00003787254,0.00001259558,0.00006683207,0.00010791,0.00008024486],"domain_scores_gemma":[0.9988093,0.0005752956,0.0001392556,0.0001706379,0.0002664169,0.00003894977],"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":[0.0006113054,0.0003107618,0.01857277,0.0002381765,0.00008658503,0.0004339919,0.0001622647,0.5378855,0.3359823,0.009119571,0.00139601,0.0952008],"study_design_scores_gemma":[0.00001065557,0.0003905316,0.009349025,0.00001332273,0.00003478208,0.0002034998,0.00008465183,0.9062126,0.08040585,0.002048086,0.001223169,0.00002381127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436896,0.0003699563,0.04877233,0.0001780863,0.00001740819,0.00003756756,0.0001369652,0.0003198909,0.006478144],"genre_scores_gemma":[0.9916058,0.00005934704,0.007488299,0.00001876539,0.000004914534,0.00001423357,0.000063294,0.00002494817,0.0007203658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002502874,"threshold_uncertainty_score":0.008373022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05992243333814228,"score_gpt":0.2930187751475127,"score_spread":0.2330963418093704,"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."}}