{"id":"W4416342724","doi":"10.1109/socc66126.2025.11235425","title":"Hardware-aware Gradient-based Column-wise Mixed-Precision Quantization for Compute-in-Memory Accelerators","year":2025,"lang":"","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"King Abdullah University of Science and Technology","keywords":"Quantization (signal processing); Context (archaeology); Noise (video); Product (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":[0.0003232327,0.0007228735,0.0006704716,0.0004032151,0.0004220822,0.001009435,0.001340637,0.0003830224,0.004908893],"category_scores_gemma":[0.001323476,0.000272581,0.0001903799,0.0006201788,0.000346454,0.001084748,0.0008801491,0.0009314753,0.001060873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000501717,"about_ca_system_score_gemma":0.001399127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003022016,"about_ca_topic_score_gemma":0.01008044,"domain_scores_codex":[0.9995865,0.0000558944,0.00002306817,0.00006093949,0.0002256455,0.00004789239],"domain_scores_gemma":[0.999549,0.00009395553,0.0000363189,0.00008096491,0.0002043812,0.00003537082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001100389,0.000303556,0.001380823,0.0004835339,0.0001092341,0.0001350484,0.0001875325,0.1072211,0.1357274,0.03604191,0.03055435,0.6867551],"study_design_scores_gemma":[0.00005544637,0.0001642408,0.0002715559,0.0000296894,0.00001883846,0.00009233896,0.00003340279,0.9419433,0.04205491,0.009258737,0.006047556,0.00002988319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03188561,0.001993733,0.9546341,0.0005080855,0.0002997935,0.00006863117,0.0003363585,0.004150074,0.006123493],"genre_scores_gemma":[0.4699439,0.0004474804,0.5219492,0.0003281504,0.00009255205,0.00008005185,0.0005528726,0.0002725104,0.00633332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004908893,"threshold_uncertainty_score":0.01642185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477282613980064,"score_gpt":0.2975486899614779,"score_spread":0.2727758638216773,"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."}}