{"id":"W3102545030","doi":"10.36227/techrxiv.13238423.v1","title":"second photonic convolutional accelerator for deep learning optical neural networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Tera-; Pixel; Bottleneck; Optical computing; Artificial neural network; Scalability; FLOPS; Computer hardware; Pattern recognition (psychology); Computer vision; Parallel computing; Electronic engineering; Embedded system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002056147,0.0003259208,0.00021504,0.00022308,0.0003001658,0.0004845388,0.0007181379,0.0004611052,0.006454799],"category_scores_gemma":[0.0004210705,0.0001417545,0.0001435091,0.0002116332,0.0002675991,0.0006587653,0.0005205118,0.0007042489,0.001106858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085634,"about_ca_system_score_gemma":0.0006808923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120559,"about_ca_topic_score_gemma":0.00185808,"domain_scores_codex":[0.9998429,0.00001100252,0.000004873687,0.00002257821,0.00008708915,0.00003155234],"domain_scores_gemma":[0.99984,0.00003065554,0.00002097758,0.0000274801,0.00005643382,0.00002444229],"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":[0.0007447105,0.0004543145,0.00258906,0.0003584472,0.00008895584,0.0005129684,0.0002024443,0.04022782,0.5379533,0.1190907,0.04534014,0.2524371],"study_design_scores_gemma":[0.00005420203,0.0003927253,0.0008751244,0.0000352299,0.00002366965,0.0003455915,0.00002423605,0.5595605,0.3481513,0.008154999,0.08235099,0.0000315625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2586689,0.001693603,0.6382004,0.002774436,0.00163583,0.0002235968,0.0007395719,0.01220505,0.08385865],"genre_scores_gemma":[0.7916139,0.0003353712,0.1666704,0.0004690333,0.0001011238,0.0001302606,0.0004724136,0.0002298692,0.03997763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006454799,"threshold_uncertainty_score":0.02159345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03174854661982648,"score_gpt":0.2615420106392592,"score_spread":0.2297934640194327,"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."}}