{"id":"W4410536797","doi":"10.1109/jlt.2025.3571748","title":"A Microwave Photonic Processor for Convolutional Neural Networks With Increased Effective Speed of Convolution","year":2025,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolution (computer science); Convolutional neural network; Photonics; Microwave; Computer science; Kernel (algebra); Electronic engineering; Artificial neural network; Parallel computing; Optics; Artificial intelligence; Physics; Telecommunications; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004208658,0.0001785003,0.0004945493,0.0005661522,0.0001223474,0.00003256057,0.0007976047,0.0002061717,0.000001390239],"category_scores_gemma":[0.0001128648,0.0001267889,0.0001523527,0.001103294,0.0002208521,0.0001960227,0.0001960479,0.000434782,2.530311e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009780095,"about_ca_system_score_gemma":0.0002180295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004819181,"about_ca_topic_score_gemma":0.000009374996,"domain_scores_codex":[0.9985751,0.00006670843,0.0005756634,0.0002531111,0.000188477,0.0003409335],"domain_scores_gemma":[0.9976522,0.0003792011,0.0007957428,0.0002515521,0.0008670124,0.00005433375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005846398,0.002123447,0.04298952,0.001297433,0.003240245,0.0005670366,0.0004497335,0.1429832,0.3715978,0.3078848,0.007801655,0.1132188],"study_design_scores_gemma":[0.003736253,0.001915639,0.002853899,0.0005259285,0.00009626924,0.0006817785,0.00003934074,0.8949371,0.08261417,0.0118196,0.0005409278,0.0002391277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4029215,0.001219134,0.593434,0.001500885,0.0003521424,0.0004554591,0.000001195273,0.00005229318,0.00006338909],"genre_scores_gemma":[0.9757608,0.00001610009,0.02396073,0.0001185734,0.0000991591,0.00001052045,9.960219e-7,0.000008406603,0.00002470333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7519539,"threshold_uncertainty_score":0.5170301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005016224766957465,"score_gpt":0.2313951163187656,"score_spread":0.2263788915518081,"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."}}