On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training

Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +3

Aiming to accelerate the training of large deep neural networks (DNN) in an energy-efficient way, analog in-memory computing (AIMC) emerges as a solution with immense potential. AI…