{"id":"W4389584362","doi":"10.1364/fio.2023.fw6e.4","title":"Fully Integrated Photonic Tensor Core Accelerator for Neural Network Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Photonics; Artificial neural network; Computer science; Core (optical fiber); Integrated optics; Photonic integrated circuit; Physics; Artificial intelligence; Optoelectronics; Telecommunications","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":[0.0002133656,0.0001621289,0.0001784372,0.00006984641,0.0004059921,0.0001760515,0.001041561,0.00006642225,0.00001176055],"category_scores_gemma":[0.0000133372,0.0001198878,0.0001173477,0.001648804,0.0000291316,0.000180667,0.0003664258,0.0001626151,0.00009158308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002130723,"about_ca_system_score_gemma":0.00005533331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001108421,"about_ca_topic_score_gemma":0.00001797361,"domain_scores_codex":[0.9985242,0.00002314672,0.0002570198,0.0004625649,0.0001518548,0.0005811387],"domain_scores_gemma":[0.9988937,0.0002531446,0.0000761314,0.000499927,0.000152724,0.0001243479],"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":[0.00003905683,0.0001080902,0.002315748,0.00008157902,0.00009294083,0.00003574708,0.0001566378,0.4276498,0.0031409,0.1092441,0.316556,0.1405794],"study_design_scores_gemma":[0.0001999415,0.0000598419,0.0006064576,0.00001032137,0.000003543304,0.000009062005,0.0000155965,0.9350336,0.0001539355,0.002200322,0.0615391,0.0001683247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1634859,0.0002823699,0.8194643,0.005795634,0.001930785,0.002909432,0.00001290671,0.004079118,0.002039567],"genre_scores_gemma":[0.937122,0.00003080037,0.05519357,0.002205461,0.001090747,0.0005940282,0.00004589438,0.0000391331,0.003678325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7736362,"threshold_uncertainty_score":0.4888884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04771239198957271,"score_gpt":0.2868222063031489,"score_spread":0.2391098143135761,"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."}}