{"id":"W7065105565","doi":"","title":"Development of a Fixed-Point Deep Neural Networks Library in C++ and its use to validate Photonic Neuromorphic Accelerators","year":2021,"lang":"en","type":"article","venue":"Electronic Theses and Dissertations Repository (University of Pisa)","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neuromorphic engineering; Artificial neural network; Photonics; Exploit; Field (mathematics); Energy consumption; Software; Inference; Deep learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001707498,0.001484191,0.0005440826,0.0008694209,0.0004791321,0.001489049,0.003600996,0.001006222,0.01850692],"category_scores_gemma":[0.005853558,0.0007825381,0.0009762173,0.0006570503,0.0006452441,0.001476708,0.001006813,0.003067958,0.0107849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218351,"about_ca_system_score_gemma":0.002711787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005014441,"about_ca_topic_score_gemma":0.004243759,"domain_scores_codex":[0.9987028,0.0001642053,0.0001040263,0.0001968578,0.0006881443,0.0001439386],"domain_scores_gemma":[0.9977269,0.0007669321,0.0001230328,0.0002734229,0.001019479,0.00009041035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001197527,0.0006672372,0.003840872,0.002600047,0.0002707706,0.0009710559,0.0005196265,0.2431597,0.06858017,0.06594443,0.1507175,0.461531],"study_design_scores_gemma":[0.0002857311,0.0002896716,0.0009252163,0.0004053864,0.00006311121,0.0002586553,0.00006700979,0.7225752,0.1316471,0.01087257,0.1324709,0.0001395142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02483683,0.0005114952,0.8146779,0.0006118141,0.0004485443,0.000668173,0.005932915,0.1303487,0.02196371],"genre_scores_gemma":[0.1164108,0.0009416541,0.8248098,0.0009294228,0.0000659764,0.001859587,0.01245077,0.02316821,0.01936375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01850692,"threshold_uncertainty_score":0.06191182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274097614519838,"score_gpt":0.2013689204766702,"score_spread":0.1886279443314718,"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."}}