{"id":"W2884288768","doi":"10.1109/fpl.2018.00077","title":"DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Ferroelectric and Negative Capacitance Devices","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Compiler; Very long instruction word; Overlay; Field-programmable gate array; Computer architecture; Deep learning; Overhead (engineering); Recurrent neural network; Embedded system; Parallel computing; Artificial neural network; Artificial intelligence; Operating system","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":[0.00009450599,0.0002469773,0.0002792545,0.0000429655,0.00007325335,0.0001174508,0.0001299556,0.0002106554,0.00009553085],"category_scores_gemma":[0.00002304007,0.0002254352,0.00005258621,0.00007924108,0.00004850255,0.0001268378,0.00007678046,0.0002763984,0.00000857011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004301315,"about_ca_system_score_gemma":0.00001778702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007494946,"about_ca_topic_score_gemma":0.00005659966,"domain_scores_codex":[0.999141,0.00001605347,0.0002130539,0.0002721318,0.00008940842,0.0002683989],"domain_scores_gemma":[0.9994273,0.0001757274,0.00005102615,0.0001724338,0.0001176227,0.00005589598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001686206,0.00005112404,0.007973914,0.002521469,0.0006734936,0.000003983232,0.001451043,0.4410524,0.001312156,0.01037438,0.4638049,0.07061252],"study_design_scores_gemma":[0.0003067839,0.00009223236,0.01011241,0.000127597,0.00004131748,0.000001552165,0.00002016521,0.9713173,0.001404797,0.006349718,0.009707686,0.0005184714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7207282,0.004026457,0.244312,0.0002225247,0.003681091,0.001772171,0.0001010958,0.001009975,0.02414659],"genre_scores_gemma":[0.9935658,0.0003270842,0.004476673,0.0001787617,0.0009901796,0.0001251626,0.00008472006,0.00003334029,0.0002182959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5302649,"threshold_uncertainty_score":0.9192981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03578349572180349,"score_gpt":0.2690577582981956,"score_spread":0.2332742625763921,"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."}}