{"id":"W2954615675","doi":"","title":"Variable Precision Floating-Point RISC-V Coprocessor Evaluation using Lightweight Software and Compiler Support","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada)","funders":"","keywords":"Computer science; Compiler; Floating point; Coprocessor; Speedup; Parallel computing; Reduced instruction set computing; Variable (mathematics); Double-precision floating-point format; Instruction set; Single-precision floating-point format; Computational science; Programming language; Mathematics","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":[0.01287657,0.0004489806,0.0006150204,0.0002305415,0.000458041,0.001020532,0.001859611,0.0003647011,0.0002241241],"category_scores_gemma":[0.002716854,0.0004311116,0.0001533867,0.0006055488,0.0001429905,0.0005975486,0.003488874,0.0007487372,0.00005023306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001878141,"about_ca_system_score_gemma":0.0007117487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004347815,"about_ca_topic_score_gemma":0.00002359093,"domain_scores_codex":[0.9906496,0.005749987,0.0007769804,0.001435864,0.0009113029,0.0004761986],"domain_scores_gemma":[0.9907815,0.002149164,0.0008522533,0.002554749,0.003404321,0.0002579818],"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":[0.00002482421,0.00113172,0.00260565,0.0009637686,0.0002038586,0.000009680342,0.01049229,0.003483445,0.007251955,0.07049838,0.001840456,0.901494],"study_design_scores_gemma":[0.0006910036,0.000001988333,0.0009674444,0.00179346,0.00008144541,0.00002412245,0.0000274866,0.9149023,0.02021962,0.0530582,0.007554856,0.0006780965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008117616,0.0009257573,0.9797012,0.001682395,0.000734324,0.0008884849,0.00003815712,0.000266213,0.00764585],"genre_scores_gemma":[0.02910614,0.00017996,0.9686798,0.000139287,0.00004693584,0.00004763088,0.0001582514,0.00004621326,0.001595821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9114189,"threshold_uncertainty_score":0.9998141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02770308438994855,"score_gpt":0.2814582680336584,"score_spread":0.2537551836437098,"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."}}