{"id":"W4323777068","doi":"10.2139/ssrn.4382942","title":"Pipeline Dnn Model Parallelism for Improving Performance of Embedded Applications","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Parallelism (grammar); Pipeline (software); Computer science; Parallel computing; Data parallelism; Task parallelism; Computer architecture; 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":[0.0002389656,0.0006536598,0.0003635358,0.0002961152,0.0002442153,0.0003924845,0.0008565982,0.0002642376,0.004425018],"category_scores_gemma":[0.0006261151,0.0001796941,0.0003055924,0.0004351667,0.0001421318,0.0009432471,0.0003773017,0.0006574822,0.0008930516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005536165,"about_ca_system_score_gemma":0.0009580262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005467905,"about_ca_topic_score_gemma":0.01190005,"domain_scores_codex":[0.9998889,0.00001488953,0.000006369316,0.00003966752,0.00002983401,0.00002044727],"domain_scores_gemma":[0.9998266,0.00004762103,0.00001395514,0.0000325861,0.00006513618,0.00001415126],"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":[0.0005593947,0.0002817612,0.001777413,0.0003043448,0.00009741107,0.000125971,0.00006711285,0.6316081,0.06080352,0.01423252,0.01114276,0.2789997],"study_design_scores_gemma":[0.000008432045,0.00005139879,0.0001400432,0.000004326935,0.00001610186,0.000009992697,0.000005488171,0.9894527,0.006105673,0.002789296,0.001412719,0.000003755794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1232139,0.001343585,0.8556043,0.0004303399,0.0003428754,0.00009307933,0.0006701302,0.004116173,0.01418563],"genre_scores_gemma":[0.8122645,0.0006512206,0.1756567,0.0001788704,0.00007939917,0.0001016684,0.0009074872,0.0003156968,0.009844459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005467905,"threshold_uncertainty_score":0.01480317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013506858523938,"score_gpt":0.2659282799848425,"score_spread":0.2524214214609045,"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."}}