{"id":"W4376653876","doi":"10.48550/arxiv.2305.08415","title":"Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Computer science; Computer hardware; System on a chip; Floating point; Hardware acceleration; Embedded system; Field-programmable gate array; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001136208,0.0005014884,0.000425198,0.0003837276,0.0002003338,0.0001797413,0.0003367216,0.0002549329,0.00006794457],"category_scores_gemma":[0.00001707777,0.0005803504,0.0001151715,0.0003975227,0.0001059631,0.0001423814,0.0004189617,0.00067264,0.0002082059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003182151,"about_ca_system_score_gemma":0.00007267724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000223179,"about_ca_topic_score_gemma":0.0002333489,"domain_scores_codex":[0.9982052,0.00006501641,0.000213873,0.0009067049,0.0001229021,0.0004862367],"domain_scores_gemma":[0.9988998,0.00009163501,0.00008854274,0.0005872043,0.0001044062,0.0002284412],"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":[0.00008734099,0.00002781741,0.001741975,0.0001321832,0.0002998349,0.0006899159,0.0005891985,0.9942028,0.0007913791,0.0003247256,0.0005422652,0.0005705658],"study_design_scores_gemma":[0.001028701,0.0001453723,0.003018788,0.0006193153,0.0003968342,0.00005135612,0.0005972951,0.9863271,0.003586839,0.001368905,0.001381489,0.001478045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461315,0.0001055666,0.05084911,0.00009502944,0.0003897948,0.0004683281,0.00009372827,0.0009177787,0.000949179],"genre_scores_gemma":[0.9975987,0.000203688,0.0005176304,0.0001055672,0.0001427776,0.000002707133,0.00004172989,0.0001323868,0.001254818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05146721,"threshold_uncertainty_score":0.9996648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06277127721949553,"score_gpt":0.1812610399617819,"score_spread":0.1184897627422863,"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."}}