{"id":"W3180565565","doi":"10.3390/app11136232","title":"Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Photonics; Computer science; Silicon on insulator; Silicon; Silicon photonics; Optoelectronics; Electronic engineering; Materials science; 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":[0.0006501069,0.0002288561,0.0002650326,0.00006872956,0.0009386321,0.0007367209,0.001710837,0.00008488341,0.00009558854],"category_scores_gemma":[0.0000432095,0.0001738775,0.00007913879,0.003238516,0.0002054455,0.0004663696,0.0003466393,0.0003485398,0.00009580029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002629171,"about_ca_system_score_gemma":0.000460049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003122399,"about_ca_topic_score_gemma":0.00002979178,"domain_scores_codex":[0.9974018,0.0000755527,0.0003395554,0.000896408,0.0004857894,0.0008008396],"domain_scores_gemma":[0.9989276,0.0001828772,0.0001491817,0.0004277841,0.0001375807,0.0001750013],"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":[0.00003110579,0.0002334091,0.001908471,0.00007049329,0.00007333825,0.0003295968,0.001139586,0.6574306,0.02126462,0.1661463,0.01291814,0.1384544],"study_design_scores_gemma":[0.0003672898,0.0001525408,0.0002253761,0.00007768078,0.000008148748,0.0001262823,0.0003335615,0.8962968,0.06830916,0.0135398,0.01990852,0.000654838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9028989,0.001274103,0.011961,0.002988229,0.002079142,0.000411667,8.913633e-7,0.0006552402,0.07773082],"genre_scores_gemma":[0.9813389,0.00004555539,0.01597347,0.00174907,0.0002819136,0.0000210516,0.000002746792,0.00001068756,0.0005765589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2388662,"threshold_uncertainty_score":0.7219294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571606081219082,"score_gpt":0.2601914235928488,"score_spread":0.234475362780658,"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."}}