{"id":"W4403278303","doi":"10.1109/fpl64840.2024.00034","title":"A Software-Programmable Neural Processing Unit for Graph Neural Network Inference on FPGAs","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Inference; Software; Graph; Computer architecture; Artificial intelligence; Parallel computing; Embedded system; Theoretical computer science; 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":[0.0001494129,0.0001942817,0.0001527901,0.00007126731,0.0003547494,0.001025231,0.0007030499,0.00005458061,0.000009740271],"category_scores_gemma":[0.0000169256,0.0001507301,0.0001112294,0.00119672,0.00004812358,0.0005265514,0.0001504639,0.0002135881,0.00002674541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001228256,"about_ca_system_score_gemma":0.00006954522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160402,"about_ca_topic_score_gemma":0.00002393563,"domain_scores_codex":[0.9984506,0.00002247804,0.0002219052,0.0005605098,0.0001825576,0.000561984],"domain_scores_gemma":[0.9990576,0.0002993519,0.00004291204,0.0003946089,0.00007570612,0.0001298525],"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":[0.0000064206,0.00003156129,0.0001278173,0.00004805368,0.000009113125,0.000007680719,0.00007311543,0.05091922,0.0000266866,0.1663081,0.006967645,0.7754745],"study_design_scores_gemma":[0.00009869875,0.0001469278,0.0001492148,0.00006238766,0.000008650129,0.00001146811,0.000005681683,0.916868,0.00005497848,0.04599717,0.03638298,0.0002138954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00866942,0.0005635753,0.9839868,0.003549068,0.0005613897,0.0005863215,0.000002891179,0.001463717,0.0006167988],"genre_scores_gemma":[0.9198267,0.00001504737,0.0764975,0.001500594,0.000460636,0.000354592,0.00001086693,0.00002517234,0.001308825],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9111574,"threshold_uncertainty_score":0.9886331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148215733870114,"score_gpt":0.3073724189518651,"score_spread":0.265890261613164,"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."}}