Summary of Redistill: Residual Encoded Distillation For Peak Memory Reduction, by Fang Chen et al.
ReDistill: Residual Encoded Distillation for Peak Memory Reduction
by Fang Chen, Gourav Datta, Mujahid Al Rafi, Hyeran Jeon, Meng Tang
First submitted to arxiv on: 6 Jun 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 paper proposes a novel approach called Residual Encoded Distillation (ReDistill) to reduce the peak memory consumption of neural networks, enabling deployment on edge devices with limited memory. By using a teacher-student framework, ReDistill derives a student network with less memory from a teacher network, applying aggressive pooling to reduce feature map sizes. The method is evaluated on computer vision tasks such as image classification and diffusion-based image generation. For image classification, ReDistill achieves 2x-3.2x peak memory reduction on an edge GPU while maintaining accuracy for most CNN architectures. Additionally, it improves test accuracy for tiny Vision Transformer (ViT) models distilled from large CNN teacher architectures. In diffusion-based image generation, ReDistill yields a denoising network with 4x lower theoretical peak memory while preserving diversity and fidelity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make neural networks more efficient and suitable for use on devices like smartphones or smart cameras. It does this by teaching smaller “student” networks to mimic the behavior of larger “teacher” networks, using a technique called distillation. This allows the student networks to learn from the teacher networks without needing as much memory or computing power. The paper shows that this approach can work well for tasks like recognizing objects in images and generating new images. |
Keywords
» Artificial intelligence » Cnn » Diffusion » Distillation » Feature map » Image classification » Image generation » Vision transformer » Vit
