{"id":"W4402475889","doi":"10.1109/hcs61935.2024.10664910","title":"Picasso: An Area/Energy-Efficient End-to-End Diffusion Accelerator with Hyper-Precision Data Type","year":2024,"lang":"en","type":"article","venue":"","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"PICASSO; Computer science; Diffusion; End-to-end principle; Energy (signal processing); Type (biology); Artificial intelligence; Physics; Art; Art history; Geology; Statistics; Mathematics","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":[0.0001519107,0.0003958677,0.0002494127,0.0002683743,0.0002276659,0.000447967,0.001009016,0.0002853753,0.006553179],"category_scores_gemma":[0.0003628141,0.0001852425,0.0001789435,0.0002758584,0.000222774,0.0006960804,0.0004816784,0.0006673908,0.001180587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005176389,"about_ca_system_score_gemma":0.000708468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113174,"about_ca_topic_score_gemma":0.002051051,"domain_scores_codex":[0.9998913,0.000007081622,0.000004137037,0.00001899273,0.00005763567,0.00002077638],"domain_scores_gemma":[0.9998651,0.00002553915,0.00002211503,0.00001534881,0.00004745358,0.0000244347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002444523,0.000384886,0.005608608,0.0007738945,0.0001650694,0.0006626336,0.0003102809,0.04890511,0.5234024,0.0340991,0.0680757,0.3151677],"study_design_scores_gemma":[0.0004272011,0.001315943,0.002287362,0.00007049925,0.0000954732,0.001038356,0.00008256578,0.440094,0.3949321,0.005372633,0.1541614,0.0001224093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2680574,0.003318267,0.6521211,0.001450612,0.001038276,0.0003435612,0.001215367,0.02032182,0.05213355],"genre_scores_gemma":[0.7382964,0.0007537465,0.2137732,0.0006183925,0.0001035936,0.0001788553,0.001393831,0.0006466841,0.04423523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006553179,"threshold_uncertainty_score":0.02192253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04277062954618508,"score_gpt":0.2817623939137339,"score_spread":0.2389917643675488,"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."}}