{"id":"W7132988363","doi":"","title":"Architecture and CAD Techniques for Efficient FPGA Implementation of Machine Learning and Other Applications","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Flexibility (engineering); Convolutional neural network; Convolution (computer science); Software; Architecture; Reconfigurable computing; Kernel (algebra); Artificial neural network","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":[0.0002575549,0.0004721594,0.0001757466,0.0005115516,0.0002222408,0.000523135,0.0005149387,0.0003275999,0.009582026],"category_scores_gemma":[0.0006313493,0.000314129,0.0003556678,0.0004155233,0.000166803,0.0004852527,0.0002130407,0.0007229535,0.001780576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005322082,"about_ca_system_score_gemma":0.0008187696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001275451,"about_ca_topic_score_gemma":0.002918359,"domain_scores_codex":[0.9997761,0.00002483963,0.00001337467,0.00002478369,0.0001359696,0.00002501402],"domain_scores_gemma":[0.9997637,0.00006287931,0.00002792041,0.00005812308,0.0000801755,0.000007195037],"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":[0.0001547193,0.00009697233,0.001438833,0.0007081469,0.00005157313,0.0003098876,0.0001660374,0.1294499,0.2152441,0.04719546,0.01060211,0.5945822],"study_design_scores_gemma":[0.0001334433,0.0005991916,0.002867415,0.0001984501,0.00007657063,0.001005956,0.00009208389,0.6088871,0.2002192,0.01312084,0.1727454,0.00005438429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04344779,0.001396285,0.9151463,0.0004199944,0.0001854749,0.0001917768,0.0002562985,0.003825648,0.03513044],"genre_scores_gemma":[0.2865945,0.001039968,0.6976366,0.0001417689,0.00004189679,0.0001756117,0.0005252364,0.0003306856,0.01351365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009582026,"threshold_uncertainty_score":0.03205502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0090377371999655,"score_gpt":0.3365282065094836,"score_spread":0.3274904693095181,"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."}}