294 citations · 898 across the 16 of their papers we have counts for
Showing 2023 · cs.LGShow all
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cs.LG2023★ 37 cited
Fast offset corrected in-memory training
Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe +1
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory c…
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…