Summary of Peano-vit: Power-efficient Approximations Of Non-linearities in Vision Transformers, by Mohammad Erfan Sadeghi et al.
PEANO-ViT: Power-Efficient Approximations of Non-Linearities in Vision Transformers
by Mohammad Erfan Sadeghi, Arash Fayyazi, Seyedarmin Azizi, Massoud Pedram
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to deploying Vision Transformers (ViTs) on Field-Programmable Gate Arrays (FPGAs), called PEANO-ViT, addresses the challenges posed by the computational and power requirements of ViT’s non-linear functions. PEANO-ViT streamlines layer normalization by introducing a division-free technique and approximates softmax using a multi-scale division strategy. Additionally, it provides a piece-wise linear approximation for Gaussian Error Linear Unit (GELU). The comprehensive evaluations show that PEANO-ViT achieves significant power efficiency improvements, reducing DSP, LUT, and register counts for non-linear operations while maintaining minimal accuracy degradation. This enables efficient deployment of ViTs on resource- and power-constrained FPGAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with using special computer chips (FPGAs) to run powerful AI models called Vision Transformers (ViTs). These chips are limited by their resources, making it hard to implement the complex math operations needed for ViT. The new approach, PEANO-ViT, makes these calculations more efficient and requires less power, while still getting accurate results. This is important because it could enable using these powerful AI models in real-world applications where power consumption is a concern. |
Keywords
» Artificial intelligence » Softmax » Vit
