{"id":"W2293057678","doi":"10.1145/2847263.2847294","title":"A Scalable Heterogeneous Dataflow Architecture For Big Data Analytics Using FPGAs (Abstract Only)","year":2016,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Dataflow; Scalability; SPARK (programming language); Field-programmable gate array; Analytics; Big data; Computer architecture; Dataflow architecture; Data analysis; Symmetric multiprocessor system; Distributed computing; Parallel computing; Embedded system; Operating system; Database; Programming language","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":[0.0003583535,0.0001662177,0.0001820076,0.0001423795,0.000153205,0.0002031677,0.002169692,0.00007373251,0.00000972919],"category_scores_gemma":[0.0001177776,0.0001100674,0.00005483198,0.0002566017,0.00004177683,0.0003480293,0.0008904899,0.00006432305,0.00001063319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003506932,"about_ca_system_score_gemma":0.0001473032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003848918,"about_ca_topic_score_gemma":0.00003513329,"domain_scores_codex":[0.998521,0.00002831539,0.0002681472,0.0006343522,0.0001942364,0.0003539291],"domain_scores_gemma":[0.9976246,0.0001882545,0.0001110271,0.001872252,0.00009737821,0.0001065034],"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":[0.00003346824,0.0002320265,0.0003927998,0.00006129331,0.0001275326,0.00002704334,0.00008305575,0.1155651,0.004631903,0.003509035,0.03102171,0.8443151],"study_design_scores_gemma":[0.0003576892,0.0000647995,0.00003068959,0.00005860633,0.00001330246,0.00007724614,0.000001084922,0.9526635,0.007406695,0.002996866,0.03603283,0.000296645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001521095,0.00004861265,0.9962034,0.000711939,0.0002650875,0.0001971814,0.0000663981,0.0005228632,0.0004634766],"genre_scores_gemma":[0.1325235,0.0000211792,0.8661623,0.0003730992,0.0002448364,0.00000314067,0.00003196926,0.00001788981,0.000622039],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8440184,"threshold_uncertainty_score":0.4488417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110060519611358,"score_gpt":0.3115332711470172,"score_spread":0.2014727515356592,"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."}}