{"id":"W2963021833","doi":"10.1109/tit.2017.2673805","title":"On the VLSI Energy Complexity of LDPC Decoder Circuits","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Very-large-scale integration; Low-density parity-check code; Decoding methods; Computer science; Electronic circuit; Energy (signal processing); Forward error correction; Electronic engineering; Algorithm; Mathematics; Electrical engineering; Engineering; Embedded system","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":[0.0008797382,0.0007812469,0.0005871827,0.000745965,0.000468056,0.001625317,0.001258098,0.0008925569,0.00831791],"category_scores_gemma":[0.01058979,0.0004749894,0.0005091832,0.001029558,0.0008405994,0.002576853,0.0008732283,0.001305175,0.0007236738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002563135,"about_ca_system_score_gemma":0.001473799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001932683,"about_ca_topic_score_gemma":0.003636975,"domain_scores_codex":[0.9987823,0.0003100361,0.00004881687,0.0001898378,0.0004991304,0.0001698472],"domain_scores_gemma":[0.9928212,0.005991058,0.000345304,0.0003646438,0.0003820753,0.00009573048],"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":[0.0006171369,0.0001598813,0.003119375,0.0003713372,0.00009253746,0.0003276936,0.0001392092,0.7291299,0.0194307,0.1629987,0.00464023,0.0789732],"study_design_scores_gemma":[0.00004013926,0.00007706998,0.0007222484,0.00002247635,0.00002370757,0.00008756718,0.00002674683,0.9339694,0.003156481,0.06087707,0.0009839092,0.00001310722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4484106,0.003247097,0.490907,0.004512718,0.0001734208,0.0002110912,0.001381053,0.001105777,0.05005132],"genre_scores_gemma":[0.9338868,0.001174635,0.05086234,0.0003769317,0.0001348099,0.0002928021,0.0008360028,0.0002346394,0.01220097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00831791,"threshold_uncertainty_score":0.02782619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03635577643927789,"score_gpt":0.2647097784756325,"score_spread":0.2283540020363546,"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."}}