Summary of Poor Man’s Training on Mcus: a Memory-efficient Quantized Back-propagation-free Approach, by Yequan Zhao et al.
Poor Man’s Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach
by Yequan Zhao, Hai Li, Ian Young, Zheng Zhang
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Neural and Evolutionary Computing (cs.NE)
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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 This paper proposes a novel approach to neural network training on edge devices, which eliminates the need for back propagation (BP) and its associated challenges. The authors develop a quantized zeroth-order method that estimates gradients of model parameters, overcoming errors in low-precision settings. To improve convergence, they employ dimension reduction techniques such as node perturbation and sparse training. Experimental results demonstrate comparable performance to BP-based training on adapting pre-trained image classifiers to corrupted data on resource-constrained edge devices. This approach is suitable for scenarios where memory cost and time-to-market are critical, but latency can be tolerated. The authors’ method combines quantized zeroth-order optimization with dimension reduction techniques to train neural networks efficiently on edge devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easy to train neural networks on small devices like phones or computers. Normally, this process is hard because of limitations in processing power and memory. The researchers came up with a new way to do training that doesn’t need the usual “back propagation” method. Instead, they use a simpler method that works well even when there’s limited precision. They also added some tricks to make the training go faster. The results show that their approach is just as good as the traditional method for certain tasks. This could be useful in situations where speed and memory are important, but it’s okay if things take a little longer. |
Keywords
» Artificial intelligence » Neural network » Optimization » Precision
