{"id":"W2612864759","doi":"10.1145/3061639.3062259","title":"Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia; National Science Foundation","keywords":"Computer science; Floating point; Multiplier (economics); Inference; Artificial neural network; Adder; Latency (audio); Computer engineering; Deep neural networks; Binary number; Overhead (engineering); Computer hardware; Hardware acceleration; Deep learning; Algorithm; Field-programmable gate array; Artificial intelligence; Arithmetic","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":[0.0004493865,0.001037248,0.0003210268,0.0006293806,0.0003670043,0.0007934123,0.002167545,0.000397298,0.01036709],"category_scores_gemma":[0.001608613,0.0004497262,0.0004095103,0.0004688113,0.0003784858,0.001400662,0.0006996148,0.001171989,0.002441531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115901,"about_ca_system_score_gemma":0.001907161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003528654,"about_ca_topic_score_gemma":0.006685112,"domain_scores_codex":[0.9994432,0.00005293403,0.0000404753,0.0001055316,0.0002859009,0.00007184336],"domain_scores_gemma":[0.9993092,0.0001632081,0.00006389956,0.0001766217,0.0002510833,0.00003606853],"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":[0.0007273992,0.0002410155,0.002235404,0.0007270732,0.0001683777,0.0004387391,0.0002115079,0.170579,0.1046468,0.03366175,0.03232584,0.6540372],"study_design_scores_gemma":[0.0001476046,0.0002665606,0.0007369439,0.00007582733,0.00004799503,0.0002370683,0.0000449059,0.8382267,0.1137045,0.01154506,0.03492717,0.00003969195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05258694,0.001090493,0.9091401,0.0004823616,0.0004637097,0.0002837229,0.0005565,0.01789841,0.01749781],"genre_scores_gemma":[0.5295601,0.0006095538,0.4532854,0.0004187533,0.0001028812,0.0004286713,0.001269619,0.001122024,0.01320294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01036709,"threshold_uncertainty_score":0.03468138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0307591957431979,"score_gpt":0.2835629074287377,"score_spread":0.2528037116855398,"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."}}