{"id":"W4403278595","doi":"10.1109/fpl64840.2024.00019","title":"H2PIPE: High Throughput CNN Inference on FPGAs with High-Bandwidth Memory","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Throughput; Computer science; Field-programmable gate array; Bandwidth (computing); Inference; Memory bandwidth; Computer architecture; Embedded system; Parallel computing; Artificial intelligence; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003592014,0.0008981733,0.000329282,0.0004284428,0.0002909107,0.0006913359,0.001702072,0.000447121,0.01390361],"category_scores_gemma":[0.001017908,0.000438361,0.0003780361,0.0004076015,0.0003385946,0.001585425,0.000677714,0.0007885285,0.002233577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008530534,"about_ca_system_score_gemma":0.0009851918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008140903,"about_ca_topic_score_gemma":0.01552544,"domain_scores_codex":[0.9997438,0.00003002381,0.00001146753,0.00006211196,0.00009980155,0.00005286185],"domain_scores_gemma":[0.9997193,0.00009894978,0.0000246127,0.00008032983,0.00005035545,0.00002625864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002504274,0.0005344242,0.007727103,0.0008189899,0.0004895909,0.0008372043,0.0002207409,0.236998,0.08093843,0.01698036,0.1331975,0.5187532],"study_design_scores_gemma":[0.0002609492,0.0003306187,0.001482792,0.00003134038,0.00004273974,0.0001289011,0.0000413454,0.932151,0.04509557,0.003966316,0.01642496,0.00004344779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2768174,0.002061275,0.5383584,0.0009014554,0.0007283373,0.0003597706,0.003770977,0.1370012,0.04000139],"genre_scores_gemma":[0.7383119,0.0004238302,0.2419681,0.0004888714,0.00007039802,0.0002294648,0.004101564,0.001218859,0.01318698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01390361,"threshold_uncertainty_score":0.04651225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615584003647604,"score_gpt":0.2688133360497464,"score_spread":0.2526574960132704,"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."}}