Iterative Activation-based Structured Pruning. (arXiv:2201.09881v1 [cs.LG])

Web: http://arxiv.org/abs/2201.09881

Deploying complex deep learning models on edge devices is challenging because
they have substantial compute and memory resource requirements, whereas edge
devices' resource budget is limited. To solve this problem, extensive pruning
techniques have been proposed for compressing networks. Recent advances based
on the Lottery Ticket Hypothesis (LTH) show that iterative model pruning tends
to produce smaller and more accurate models. However, LTH research focuses on
unstructured pruning, which is hardware-inefficient and difficult to accelerate
on hardware platforms.

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