{"id":"W2904327456","doi":"10.1109/micro.2018.00020","title":"Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Computer science; Bandwidth (computing); Chip; Convolution (computer science); Computation; Artificial neural network; Energy (signal processing); Computer hardware; Computational science; Computer engineering; Artificial intelligence; Algorithm; Telecommunications; Physics","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":[0.0003876156,0.0008091947,0.0003577421,0.0003989038,0.0003288607,0.0008260753,0.002779492,0.0004984394,0.01615168],"category_scores_gemma":[0.001191297,0.0003626352,0.0004049431,0.0004647155,0.000414454,0.001558563,0.001742653,0.001321156,0.00277593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007822001,"about_ca_system_score_gemma":0.001025207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001509233,"about_ca_topic_score_gemma":0.002542077,"domain_scores_codex":[0.9996599,0.00002547675,0.00001981809,0.00005524258,0.0001731159,0.00006648863],"domain_scores_gemma":[0.9996394,0.00008303739,0.00005028793,0.00009035735,0.00009276345,0.00004414476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002012383,0.0005987143,0.007308615,0.001059396,0.0003052968,0.0009593832,0.0002374482,0.1040748,0.1350482,0.05983282,0.1731986,0.5153644],"study_design_scores_gemma":[0.0003358121,0.0009334422,0.001619554,0.00006496556,0.0001094167,0.0006869813,0.00004434899,0.7538179,0.1373021,0.01317617,0.09182402,0.00008527841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131383,0.002778408,0.8076201,0.001216461,0.00134656,0.000295625,0.001804222,0.03812763,0.03367264],"genre_scores_gemma":[0.6419863,0.0009024134,0.312909,0.001468707,0.0002121815,0.0003931187,0.004337099,0.001652202,0.03613906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01615168,"threshold_uncertainty_score":0.05403268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030057917802687,"score_gpt":0.2617721245926274,"score_spread":0.2414715454146006,"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."}}