Summary of Hydravit: Stacking Heads For a Scalable Vit, by Janek Haberer et al.
HydraViT: Stacking Heads for a Scalable ViT
by Janek Haberer, Ali Hojjat, Olaf Landsiedel
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces HydraViT, a novel approach that addresses the limitations of deploying Vision Transformers (ViTs) on devices with varying constraints. The architecture of ViTs imposes substantial hardware demands, particularly due to the Multi-head Attention (MHA) mechanism. To achieve scalability and adaptability across different hardware environments, HydraViT stacks attention heads, inducing multiple subnetworks during training. This approach maintains performance while covering a wide range of resource constraints. Experimental results demonstrate the efficacy of HydraViT in achieving up to 10 subnetworks, with up to 5 p.p. more accuracy with the same GMACs and up to 7 p.p. more accuracy with the same throughput on ImageNet-1K compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HydraViT is a new way to make Vision Transformers work better on devices with different amounts of power and memory. Right now, it’s hard to use these powerful models on things like phones because they need too much hardware. The paper solves this problem by stacking attention heads in the model during training, which helps it work well even when there are limited resources available. This makes it possible to get accurate results even on devices with less power and memory. |
Keywords
* Artificial intelligence * Attention * Multi head attention
