Summary of Elsa: Exploiting Layer-wise N:m Sparsity For Vision Transformer Acceleration, by Ning-chi Huang et al.
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
by Ning-Chi Huang, Chi-Chih Chang, Wei-Cheng Lin, Endri Taka, Diana Marculescu, Kai-Chiang Wu
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed ELSA method, Exploiting Layer-wise N{:}M Sparsity for ViTs, addresses the challenge of selecting suitable sparse configurations for vision transformers (ViTs) on accelerators supporting mixed sparsity. The approach considers not only the different N{:}M sparsity levels supported by a given accelerator but also the expected throughput improvement. By trading off negligible accuracy loss with memory usage and inference time reduction, ELSA achieves a 2.9reduction in FLOPs for both Swin-B and DeiT-B models on ImageNet with only a marginal degradation of accuracy. The method’s code will be released upon paper acceptance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method called ELSA to help accelerators speed up deep neural networks by finding the best way to compress them. Usually, these networks have lots of layers that work together in special ways. The challenge is to figure out which parts of the network should be compressed differently depending on the specific hardware used. ELSA does this by considering not just how many computations are needed but also how fast the computer can do those calculations. This helps reduce both memory usage and time it takes to make predictions while still keeping the accuracy good enough. |
Keywords
» Artificial intelligence » Inference
