{"id":"W3162667157","doi":"10.1109/icassp39728.2021.9414908","title":"High-Throughput VLSI Architecture for Soft-Decision Decoding with ORBGRAND","year":2021,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Computer science; Throughput; Very-large-scale integration; Code (set theory); Algorithm; Parallel computing; Block (permutation group theory); Reliability (semiconductor); Error detection and correction; Code rate; Computer engineering; Computer hardware; Embedded system; Mathematics; Telecommunications","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":[0.0001971365,0.0003747024,0.000331116,0.0005893276,0.0002877139,0.0006391159,0.001122062,0.0003495599,0.006191482],"category_scores_gemma":[0.0004612287,0.0001972869,0.0001822533,0.0005059342,0.0002047295,0.0005891774,0.0005489382,0.0003881399,0.001755951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006977561,"about_ca_system_score_gemma":0.0007987093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001433956,"about_ca_topic_score_gemma":0.002783527,"domain_scores_codex":[0.9998145,0.00002578985,0.00001151944,0.00003218426,0.00008411006,0.00003189819],"domain_scores_gemma":[0.999806,0.00004577291,0.0000216035,0.00003758106,0.00007439881,0.00001475566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008533648,0.0002275471,0.003690864,0.0007317602,0.0001399792,0.0006475553,0.0002432349,0.03569085,0.3558754,0.04212135,0.02243456,0.5373435],"study_design_scores_gemma":[0.0003186455,0.001751574,0.003142103,0.0001719028,0.0001807186,0.001681705,0.0001280449,0.4439947,0.4322921,0.01155806,0.1046148,0.0001656288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2022398,0.002603153,0.734858,0.0008463323,0.0003350562,0.0002883883,0.0008263616,0.01456258,0.04344033],"genre_scores_gemma":[0.7122772,0.0005025605,0.2735677,0.0003969303,0.00006718135,0.0001423535,0.001009925,0.0001455029,0.01189066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006191482,"threshold_uncertainty_score":0.02071261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402993869607088,"score_gpt":0.264141964560198,"score_spread":0.2501120258641271,"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."}}