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

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

eess.SP2023

Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

An Chen, Wenbo Xu, Liyang Lu +1

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas…

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…