{"id":"W2140405585","doi":"10.1109/sips.2007.4387554","title":"An Area-Efficient FPGA-Based Architecture for Fully-Parallel Stochastic LDPC Decoding","year":2007,"lang":"en","type":"article","venue":"SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Low-density parity-check code; Decoding methods; Computer science; Field-programmable gate array; Berlekamp–Welch algorithm; Virtex; Throughput; Parallel computing; List decoding; Algorithm; Concatenated error correction code; Error floor; Computer hardware; Wireless; Telecommunications; Block code","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002883348,0.0004134536,0.0003694917,0.0005432441,0.0007445746,0.0008335815,0.0006180556,0.0001458217,0.000006757608],"category_scores_gemma":[0.000047917,0.0003858344,0.00008543063,0.0006176416,0.00006360564,0.00036994,0.00003294863,0.0002832276,0.000002749268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002026018,"about_ca_system_score_gemma":0.0002355323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002337895,"about_ca_topic_score_gemma":0.00002830894,"domain_scores_codex":[0.9967366,0.0002442694,0.0007753773,0.0008874427,0.0006154686,0.0007407822],"domain_scores_gemma":[0.9974732,0.001058606,0.0005178869,0.00039912,0.0002910105,0.0002601439],"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":[0.0003391493,0.000185152,0.000034175,0.0002390157,0.00002742207,0.00001083872,0.003673197,0.5830081,0.01434063,0.000778903,0.0007478842,0.3966156],"study_design_scores_gemma":[0.001160268,0.0007431969,0.00004319187,0.0005818625,0.0000318349,0.00003887223,0.001729309,0.9823325,0.01212887,0.0005268932,0.0001082468,0.000575003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01206522,0.0002061904,0.9844131,0.0001494774,0.0004280369,0.001984034,0.000006066637,0.0007040647,0.00004384324],"genre_scores_gemma":[0.8476843,8.817501e-7,0.1514886,0.0003154298,0.0001700678,0.0002542839,0.00001734816,0.00004450299,0.00002455986],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8356191,"threshold_uncertainty_score":0.9998593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05856250830028428,"score_gpt":0.3525496725721451,"score_spread":0.2939871642718609,"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."}}