{"id":"W2766143712","doi":"10.1109/tcsi.2017.2757036","title":"An Architecture to Accelerate Convolution in Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Convolutional neural network; Computer science; Latency (audio); Convolution (computer science); Artificial intelligence; Contextual image classification; Process (computing); Deep neural networks; Deep learning; State (computer science); Artificial neural network; Implementation; Computational complexity theory; Pattern recognition (psychology); Low latency (capital markets); Machine learning; Computer engineering; Image (mathematics); Algorithm; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001913802,0.000207261,0.000236928,0.0001421787,0.0008044563,0.0004602939,0.0007516089,0.0001126234,0.000002557819],"category_scores_gemma":[0.000005141077,0.0001955822,0.00005340893,0.0002241121,0.00007953797,0.00049398,0.000005097902,0.000281577,0.000005090082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006068788,"about_ca_system_score_gemma":0.00001745873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000709947,"about_ca_topic_score_gemma":0.0003065166,"domain_scores_codex":[0.9984342,0.0001191953,0.0002693389,0.0006088051,0.0001977035,0.0003707218],"domain_scores_gemma":[0.9983119,0.00005687393,0.0001160061,0.001166073,0.00004413384,0.0003050352],"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":[0.000006180901,0.00003854413,0.00004456381,0.000008853586,0.000009059905,0.000009279069,0.0002512845,0.7741119,0.004960134,0.001605643,0.000007848769,0.2189467],"study_design_scores_gemma":[0.0004723439,0.0001649101,0.003433726,0.0000530754,0.00000932343,0.00007477767,0.0000638241,0.9938809,0.000414272,0.0001567406,0.0009314138,0.0003446852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03248957,0.0001113774,0.9648808,0.0007979363,0.0007004398,0.0006075216,0.000004429718,0.0001221796,0.0002857317],"genre_scores_gemma":[0.9988447,0.00003188047,0.0004534022,0.0002938499,0.00009198624,0.0001624183,0.000001320865,0.00001914045,0.0001012784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9663551,"threshold_uncertainty_score":0.797561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093066787534213,"score_gpt":0.2590709766850191,"score_spread":0.238140308809677,"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."}}