{"id":"W4413278482","doi":"10.1109/icfpt64416.2024.11113460","title":"GraphNoC: Graph Neural Networks for Application-Specific FPGA NoC Performance Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; Computer science; Artificial neural network; Computer architecture; Graph; Parallel computing; Embedded system; Artificial intelligence; Theoretical computer science","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":[0.00006223539,0.0001214157,0.00008445283,0.00007753442,0.0001074429,0.00004418543,0.00008321556,0.00005072014,0.00001119884],"category_scores_gemma":[0.00000121383,0.0001119976,0.0000693863,0.000307238,0.00001686231,0.0002556477,0.00001154051,0.0001615879,0.000008944588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001827538,"about_ca_system_score_gemma":0.000001617986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.252738e-7,"about_ca_topic_score_gemma":6.154498e-7,"domain_scores_codex":[0.9993609,0.000003512305,0.0001649492,0.0001975943,0.00005996209,0.0002131159],"domain_scores_gemma":[0.999724,0.00006831593,0.000009139201,0.000137493,0.00001791356,0.00004318241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007744393,0.000003349802,0.00007901751,0.0001036522,0.00001184805,7.412514e-7,0.0000258724,0.8329623,0.002807392,0.001390844,0.001729397,0.1608778],"study_design_scores_gemma":[0.00008367257,0.00003235395,0.0003316524,0.00002062642,0.000006398017,0.00001033024,0.000009063192,0.9753323,0.005787242,0.0003720507,0.01789523,0.0001190414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1250158,0.001865331,0.8693342,0.00002682012,0.00116641,0.0002936441,0.000005391352,0.00152768,0.0007647377],"genre_scores_gemma":[0.9978139,0.000379883,0.0009953241,0.00003119642,0.0005078406,0.00009751049,0.00002735216,0.00003320531,0.0001138014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8727981,"threshold_uncertainty_score":0.4567128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218642828372513,"score_gpt":0.2161648605500382,"score_spread":0.203978432266313,"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."}}