{"id":"W4396949517","doi":"10.1109/isqed60706.2024.10528676","title":"DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization","year":2024,"lang":"en","type":"article","venue":"","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Memory footprint; Quantization (signal processing); Footprint; Computer science; Reduction (mathematics); Layer (electronics); Artificial intelligence; Algorithm; Mathematics; Geology; Materials science; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.000353768,0.0001537538,0.000110067,0.0002650658,0.0002082114,0.0002153188,0.0001495434,0.0001026138,0.0003483882],"category_scores_gemma":[0.0004931579,0.0001371265,0.00008164508,0.0006217212,0.00007131562,0.0003243091,0.00004022359,0.0002536481,0.0005308076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001192044,"about_ca_system_score_gemma":0.00006055233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002290144,"about_ca_topic_score_gemma":0.00001130046,"domain_scores_codex":[0.9983674,0.0001536114,0.0003229588,0.0006295499,0.0003042269,0.0002222155],"domain_scores_gemma":[0.9993537,0.0001444013,0.00006325889,0.0002784268,0.00007084315,0.00008934094],"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":[0.00001717692,0.00003298641,0.000001548664,0.0000137192,0.000002199006,0.000004132802,0.000775213,0.0001066792,0.776619,0.003625563,0.00008594374,0.2187159],"study_design_scores_gemma":[0.0001777342,0.00007147839,0.0006175664,0.00003718331,0.000009566332,0.0001828899,0.0009401161,0.0975365,0.8966367,0.0005305837,0.003073685,0.0001859649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5625177,0.00006974225,0.4172749,0.00387164,0.005539773,0.0005851396,0.000004619288,0.001696999,0.008439533],"genre_scores_gemma":[0.9941746,0.00003386142,0.00179136,0.0002801458,0.0002120072,0.00003380234,0.000006273603,0.00003422707,0.003433716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4316569,"threshold_uncertainty_score":0.6822634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09691226923567263,"score_gpt":0.3212971273818072,"score_spread":0.2243848581461345,"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."}}