Summary of Hesso: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning, by Tianyi Chen et al.
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
by Tianyi Chen, Xiaoyi Qu, David Aponte, Colby Banbury, Jongwoo Ko, Tianyu Ding, Yong Ma, Vladimir Lyapunov, Ilya Zharkov, Luming Liang
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 The OTO series has revolutionized the field of structured pruning by streamlining the workflow for automatically compressing deep neural networks (DNNs) into compact sub-networks. However, existing methods require significant engineering efforts and human expertise to achieve optimal results. To address these limitations, we propose a Hybrid Efficient Structured Sparse Optimizer (HESSO), which can efficiently train DNNs to produce high-performing subnetworks without requiring hyper-parameter tuning or manual intervention. Additionally, we introduce the Corrective Redundant Identification Cycle (CRIC) to reliably identify indispensable structures and prevent irreversible performance collapse during pruning. Our experiments demonstrate that HESSO achieves competitive performance compared to state-of-the-art methods on various applications, including computer vision, natural language processing, and large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to compress deep neural networks called structured pruning. It’s like taking a big library and getting rid of unnecessary books so you can still find the important ones easily. The authors developed a special tool that can do this automatically without needing a lot of human help. They also created another tool that helps make sure the important parts of the network aren’t accidentally deleted. This makes it easier to use these networks for tasks like recognizing images or understanding language. |
Keywords
» Artificial intelligence » Natural language processing » Pruning
