{"id":"W4240155423","doi":"10.36227/techrxiv.13238423","title":"11.0 Tera-FLOP/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":0,"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; Optical computing; Artificial neural network; Bottleneck; Scalability; Computer vision; Computer hardware; Pattern recognition (psychology); 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":[0.0002415983,0.0003715776,0.0001970694,0.0002325354,0.0002071931,0.0004126563,0.0007823629,0.0003634272,0.01236047],"category_scores_gemma":[0.0004976817,0.0001472678,0.000106204,0.0002369213,0.0002217787,0.0006406329,0.0004183117,0.0007009412,0.001901097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008384263,"about_ca_system_score_gemma":0.0006045817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118262,"about_ca_topic_score_gemma":0.001843052,"domain_scores_codex":[0.9998596,0.00001177225,0.000004935859,0.00001748279,0.00008012632,0.00002618662],"domain_scores_gemma":[0.9998538,0.00003627721,0.00002140354,0.00002547657,0.00004223968,0.00002068705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00126228,0.0006995301,0.0041905,0.0005988062,0.0001205589,0.0004514831,0.0002616539,0.0480339,0.4686221,0.1065776,0.07044901,0.2987326],"study_design_scores_gemma":[0.00009087882,0.0004176206,0.001241869,0.00005012477,0.0000251967,0.0002703313,0.00003390622,0.6463626,0.2578325,0.006654416,0.08698387,0.0000365817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2900447,0.001509646,0.5956691,0.001791194,0.0008779342,0.0003065728,0.00103449,0.01880808,0.08995825],"genre_scores_gemma":[0.7543436,0.0003732763,0.2039989,0.0002773532,0.00006027218,0.000189006,0.0008178694,0.0003945974,0.0395452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01236047,"threshold_uncertainty_score":0.04134995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969683149621168,"score_gpt":0.2600974157793607,"score_spread":0.2304005842831491,"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."}}