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DRIVE: Dual Gradient-Based Rapid Iterative Pruning
April 8, 2024, 4:41 a.m. | Dhananjay Saikumar, Blesson Varghese
cs.LG updates on arXiv.org arxiv.org
Abstract: Modern deep neural networks (DNNs) consist of millions of parameters, necessitating high-performance computing during training and inference. Pruning is one solution that significantly reduces the space and time complexities of DNNs. Traditional pruning methods that are applied post-training focus on streamlining inference, but there are recent efforts to leverage sparsity early on by pruning before training. Pruning methods, such as iterative magnitude-based pruning (IMP) achieve up to a 90% parameter reduction while retaining accuracy comparable …
abstract arxiv complexities computing cs.cv cs.lg drive focus gradient inference iterative modern networks neural networks parameters performance pruning solution space space and time training type
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