{"id":"W7057778473","doi":"","title":"Low-Latency BERT Inference for heterogeneous multi-processor edge devices","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Enhanced Data Rates for GSM Evolution; Feature (linguistics); Noise (video)","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":[0.0003932548,0.0007055202,0.000448521,0.0002530367,0.0004043134,0.000656356,0.001145409,0.0004879076,0.003870097],"category_scores_gemma":[0.001357728,0.000406841,0.0003844452,0.0003323444,0.0003050158,0.001506334,0.0007215277,0.001044994,0.0005794615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009369989,"about_ca_system_score_gemma":0.001059555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008725957,"about_ca_topic_score_gemma":0.02097588,"domain_scores_codex":[0.9997905,0.00003917095,0.000009616131,0.00006443949,0.00005332047,0.00004293787],"domain_scores_gemma":[0.9997076,0.0001470707,0.00001754172,0.00004632139,0.00006308509,0.00001833902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004101683,0.000112347,0.002521082,0.0001405384,0.0000682086,0.0002536114,0.0000740014,0.7264648,0.01648593,0.01107467,0.006560508,0.2358341],"study_design_scores_gemma":[0.000006302637,0.00001829925,0.00016745,0.000003019352,0.000005444354,0.00001379643,0.00001349871,0.9927841,0.00323442,0.003130777,0.0006204474,0.000002410642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09623811,0.0005447897,0.8871028,0.000473312,0.0001206923,0.00007887782,0.0003172776,0.003690923,0.01143334],"genre_scores_gemma":[0.7514901,0.0002324226,0.239782,0.0001984114,0.00003065628,0.0000735467,0.0006720165,0.0002633236,0.007257516],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008725957,"threshold_uncertainty_score":0.01735038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873289218012019,"score_gpt":0.3024096984949624,"score_spread":0.2736768063148422,"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."}}