{"id":"W1977933385","doi":"10.1145/1629395.1629411","title":"Fine-grain performance scaling of soft vector processors","year":2009,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Personalization; Flexibility (engineering); Scaling; Parallel computing; Computer architecture; Parallelism (grammar); Embedded system; Extensibility; Vector processor; Computer hardware; 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":[0.0001815462,0.00007268575,0.0001036589,0.00007619992,0.000055861,0.00003439111,0.0004816465,0.00003200534,0.0000062596],"category_scores_gemma":[0.00003280231,0.00006149543,0.00002997017,0.0003383953,0.00001535742,0.0002093066,0.00004660146,0.00005450681,0.000006134299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008252704,"about_ca_system_score_gemma":0.00003368663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000275527,"about_ca_topic_score_gemma":4.098372e-7,"domain_scores_codex":[0.9993606,0.00001430412,0.0001766586,0.000167006,0.0001417712,0.0001397115],"domain_scores_gemma":[0.999557,0.00002059147,0.00007218547,0.0002259274,0.00009029293,0.00003395553],"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":[0.00002448347,0.0004026157,0.003903924,0.0001365261,0.00002062028,0.000004919446,0.00237642,0.1213365,0.003061603,0.09616137,0.01133739,0.7612336],"study_design_scores_gemma":[0.0001046728,0.0001194479,0.003514593,0.00003520285,0.000001102007,0.000003502975,0.000002262741,0.9512494,0.04334209,0.001166684,0.0003435339,0.0001175135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03263647,0.00004371488,0.9608285,0.0005172803,0.0000405037,0.0000598182,1.341268e-7,0.0004848081,0.005388798],"genre_scores_gemma":[0.7565936,0.000004155266,0.2428796,0.0001502016,0.00001411187,0.000001157628,4.613596e-7,0.000001906678,0.0003548739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8299129,"threshold_uncertainty_score":0.2507711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237034890989894,"score_gpt":0.2479723671027163,"score_spread":0.2356020181928173,"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."}}