{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001361634,0.0007986238,0.0002834412,0.0004426236,0.0002847372,0.0005231617,0.001324066,0.0002883848,0.01120405],"category_scores_gemma":[0.0002820446,0.000220323,0.0002201941,0.0002425379,0.0002115103,0.0006514256,0.0005821136,0.0003352699,0.002060778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007302779,"about_ca_system_score_gemma":0.0007994792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004180866,"about_ca_topic_score_gemma":0.008608338,"domain_scores_codex":[0.9998193,0.00001619075,0.000006783905,0.00005346703,0.00005426589,0.00004997177],"domain_scores_gemma":[0.9998832,0.00001435745,0.00001729896,0.00001988341,0.00004003594,0.00002519844],"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":[0.001739301,0.0005621706,0.007620388,0.0007906897,0.0002424657,0.001137974,0.0006053234,0.07060327,0.5642765,0.0120294,0.07062568,0.2697668],"study_design_scores_gemma":[0.0004521423,0.003763263,0.008780081,0.0001616328,0.0002580412,0.001154946,0.0002373451,0.4681423,0.3593014,0.002749023,0.1547655,0.0002343822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6993876,0.001573087,0.1594012,0.0005991218,0.0003361254,0.0006672523,0.002172498,0.03230133,0.1035619],"genre_scores_gemma":[0.9173597,0.0002013434,0.05290777,0.000346806,0.0000340694,0.0001856928,0.001385895,0.0004887172,0.02709012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01120405,"threshold_uncertainty_score":0.03748131,"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."}}