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cs.LG2024
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…
cs.LG2024
Towards Exact Gradient-based Training on Analog In-memory Computing
Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch +1
Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While…