{"id":"W3046245004","doi":"10.1109/ipdpsw50202.2020.00034","title":"Optimizing OpenCL Kernels and Runtime for DNN Inference on FPGAs","year":2020,"lang":"en","type":"article","venue":"","topic":"Ferroelectric and Negative Capacitance Devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Computer science; Inference; Parallel computing; Computer architecture; Embedded system; Artificial intelligence","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":[0.00003503007,0.0001020104,0.0001271164,0.00002130621,0.00003695523,0.00003676137,0.0000777805,0.00003373651,0.00007401868],"category_scores_gemma":[0.00006958,0.00008893638,0.00002185447,0.00009881458,0.00001528678,0.0001302972,0.00001259758,0.00007636754,0.00003359098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001369549,"about_ca_system_score_gemma":0.000005951217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001799955,"about_ca_topic_score_gemma":0.000001695212,"domain_scores_codex":[0.9995559,0.000004524043,0.00009408761,0.0001382359,0.00005508485,0.0001521124],"domain_scores_gemma":[0.9996939,0.0001444264,0.00001118325,0.00005581909,0.00002230766,0.00007237283],"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":[0.0006838284,0.0001584433,0.002831989,0.002511669,0.0009822738,0.00004945918,0.02770319,0.2240552,0.233338,0.1499269,0.1206873,0.2370718],"study_design_scores_gemma":[0.0008447357,0.0004153724,0.0005135636,0.00006775402,0.00002216413,0.000001264885,0.0003640865,0.8743704,0.1061827,0.0007135081,0.01605023,0.0004542386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5373563,0.001857898,0.3505211,0.001969326,0.0002338087,0.0009588854,0.00002997553,0.0009334484,0.1061392],"genre_scores_gemma":[0.9928355,0.00009865419,0.006089209,0.0007301082,0.00005500538,0.00002255589,0.000002347598,0.00001601074,0.0001506514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6503152,"threshold_uncertainty_score":0.362672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374337786329912,"score_gpt":0.2445978066192321,"score_spread":0.210854428755933,"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."}}