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cs.LG2026
Is Finer Better? The Limits of Microscaling Formats in Large Language Models
Andrea Fasoli, Monodeep Kar, Chi-Chun Liu +4
Microscaling data formats leverage per-block tensor quantization to enable aggressive model compression with limited loss in accuracy. Unlocking their potential for efficient train…
cs.LG2023★ 3 cited
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Malte J. Rasch, Charles Mackin, Manuel Le Gallo +10
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only app…