most citedHardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

3 citations · 3 across the 1 of their papers we have counts for

collaborators

1 paper

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…