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cs.LG2026
Robust Heterogeneous Analog-Digital Computing for Mixture-of-Experts Models with Theoretical Generalization Guarantees
Mohammed Nowaz Rabbani Chowdhury, Hsinyu Tsai, Geoffrey W. Burr +3
Sparse Mixture-of-Experts (MoE) models enable efficient scalability by activating only a small sub-set of experts per input, yet their massive parameter counts lead to substantial…
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…