{"id":"W2067618990","doi":"10.1145/2535932","title":"Exploiting Task- and Data-Level Parallelism in Streaming Applications Implemented in FPGAs","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; CMC Microsystems","keywords":"Computer science; Field-programmable gate array; Benchmark (surveying); Compiler; Suite; Computer architecture; Design flow; Replication (statistics); Embedded system; Implementation; Operating system; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003979306,0.000153116,0.0002466785,0.0008661277,0.000194983,0.0001282375,0.0008309361,0.0001837076,0.000008054404],"category_scores_gemma":[0.00002944559,0.000155036,0.00001261552,0.0007904464,0.00006421725,0.0004842524,0.00004207217,0.0003204303,0.000009421671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003387961,"about_ca_system_score_gemma":0.00002811598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006512744,"about_ca_topic_score_gemma":0.0001467845,"domain_scores_codex":[0.9985491,0.00007326545,0.000438578,0.0005653349,0.00008067818,0.0002930333],"domain_scores_gemma":[0.9986427,0.0001670358,0.0001054719,0.0009893174,0.00004769319,0.00004772897],"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":[0.000008091565,0.0003045283,0.005967621,0.0001544581,0.00006817274,0.00001509713,0.0007461199,0.009230142,0.001610399,0.03462305,0.0003347659,0.9469376],"study_design_scores_gemma":[0.002189624,0.0002358396,0.00547123,0.0005764501,0.00001825792,0.0002848945,0.003372781,0.9468109,0.002210343,0.03254317,0.005304652,0.000981867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02870747,0.0005919315,0.9671007,0.001825945,0.00005921504,0.000782187,0.00001567543,0.0004829758,0.000433882],"genre_scores_gemma":[0.9652293,0.0003568045,0.0335995,0.0000525943,0.000006499999,0.0006105246,0.00001138194,0.000008937889,0.0001245089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9459557,"threshold_uncertainty_score":0.6322184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04862525721537313,"score_gpt":0.289427861795615,"score_spread":0.2408026045802418,"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."}}