{"id":"W2048166678","doi":"10.1109/ipdpsw.2010.5470679","title":"A GPU-inspired soft processor for high-throughput acceleration","year":2010,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Datapath; Parallel computing; Field-programmable gate array; Multithreading; Coprocessor; Pipeline (software); Thread (computing); Computer architecture; Hardware acceleration; Register file; Instruction set; Floating point; General-purpose computing on graphics processing units; Embedded system; Graphics; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002210044,0.0001079287,0.0001117318,0.00006865807,0.0001895042,0.0002585926,0.0006034655,0.00008630192,0.00001744606],"category_scores_gemma":[0.00009767226,0.00009381375,0.00003826288,0.0002168031,0.00002137262,0.0005173537,0.0001002623,0.00009965927,0.0000178938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009953501,"about_ca_system_score_gemma":0.00007650601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998212,"about_ca_topic_score_gemma":0.00002093484,"domain_scores_codex":[0.9991283,0.00001251741,0.0001930017,0.0003327065,0.0001341484,0.0001993351],"domain_scores_gemma":[0.9992135,0.00006642791,0.00008240272,0.0003678056,0.0002171314,0.00005275357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001737181,0.0002218133,0.0002247323,0.00005235461,0.00002033769,0.000001543577,0.0006166348,0.002104026,0.01415799,0.8187532,0.03731282,0.1265171],"study_design_scores_gemma":[0.0005400861,0.00014819,0.0003751757,0.000008233677,0.000004207942,0.000006709852,0.000003644902,0.8610895,0.08706492,0.03628042,0.0141677,0.000311199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004319426,0.000006710841,0.989802,0.001756802,0.0003423399,0.0003286664,0.000001019888,0.001368413,0.002074618],"genre_scores_gemma":[0.4453094,0.000001513671,0.5533243,0.0004899323,0.0000877733,0.0000585432,0.000005477762,0.000006186429,0.0007168172],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8589855,"threshold_uncertainty_score":0.3825614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949412070424403,"score_gpt":0.2755911484620504,"score_spread":0.2560970277578064,"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."}}