{"id":"W2973613048","doi":"10.1109/tc.2019.2941875","title":"Fast and Efficient Convolutional Accelerator for Edge Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Bandwidth (computing); Throughput; Edge device; Memory bandwidth; Computer engineering; Dataflow; Parallel computing; Efficient energy use; Computer hardware; Artificial intelligence; Wireless; Cloud computing","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":[0.0001818072,0.0005660194,0.000252513,0.000355068,0.0002456857,0.0004540209,0.00102293,0.0003208613,0.005884644],"category_scores_gemma":[0.0005035091,0.0001890891,0.0002560943,0.0003625156,0.0001969828,0.0007602202,0.0005280036,0.0006854031,0.001225003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005431905,"about_ca_system_score_gemma":0.0009544916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002285309,"about_ca_topic_score_gemma":0.004961085,"domain_scores_codex":[0.9998493,0.0000111859,0.000008805056,0.00002376923,0.00006738138,0.00003954018],"domain_scores_gemma":[0.9998391,0.00003434548,0.0000182063,0.00002706792,0.00006276507,0.00001844541],"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":[0.00133667,0.0004077838,0.004737131,0.0006575713,0.00016659,0.0007463218,0.0001682887,0.1628744,0.1834105,0.0505789,0.07037918,0.5245367],"study_design_scores_gemma":[0.0000784678,0.0002731145,0.0009306666,0.00003868755,0.00004801785,0.0002470132,0.00003060242,0.8870023,0.07238813,0.006957862,0.03197329,0.00003188698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1853805,0.002233456,0.7683994,0.0008050255,0.0005951985,0.0001700395,0.0007288228,0.01172457,0.029963],"genre_scores_gemma":[0.7679323,0.0007283912,0.2082647,0.000474379,0.00007205948,0.0001495158,0.001299194,0.0002760832,0.02080346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005884644,"threshold_uncertainty_score":0.0196861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169980314743467,"score_gpt":0.2521783771278744,"score_spread":0.2351803456535277,"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."}}