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Training Deep Nets with Sublinear Memory Cost
[article]
2016
arXiv
pre-print
We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O(sqrt(n)) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the
arXiv:1604.06174v2
fatcat:e27mozwtnvfnperyfndbkzuuu4