Summary of Structurally Prune Anything: Any Architecture, Any Framework, Any Time, by Xun Wang et al.
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
by Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Neural network pruning is crucial for enhancing the efficiency of deep learning models. Structured pruning eliminates entire channels, providing direct computational and storage benefits. However, existing methods are less adaptable to different architectures, frameworks, and pruning criteria due to diverse patterns and frameworks. To address this, we introduce Structurally Prune Anything (SPA), a versatile structured pruning framework that can prune neural networks with any architecture, from any framework, and at any stage of training. SPA leverages standardized computational graphs and ONNX representations to prune diverse architectures without manual intervention. It employs group-level importance estimation, estimating importance and pruning unimportant channels. This enables the transfer of existing pruning criteria into a structured group style. SPA supports pruning at any time: before training, after training with fine-tuning, or after training without fine-tuning. In experiments, SPA shows competitive state-of-the-art pruning performance across various architectures from popular frameworks, at different pruning times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making neural networks more efficient. It’s like cleaning up a messy room – you get rid of things that aren’t needed anymore. The problem is that there are many ways to do this and it can be hard to make sure the right things get removed. To solve this, the researchers created a new way to “clean” the neural networks called Structurally Prune Anything (SPA). This method can work with different types of networks, from different places, and at different times. It’s like having a magic eraser that makes everything tidy! The paper shows that SPA is really good at making neural networks more efficient without needing extra help. |
Keywords
* Artificial intelligence * Deep learning * Fine tuning * Neural network * Pruning
