Summary of Fast and Slow Gradient Approximation For Binary Neural Network Optimization, by Xinquan Chen et al.
Fast and Slow Gradient Approximation for Binary Neural Network Optimization
by Xinquan Chen, Junqi Gao, Biqing Qi, Dong Li, Yiang Luo, Fangyuan Li, Pengfei Li
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, the authors address a challenge in optimizing Binary Neural Networks (BNNs) for deployment on edge devices. BNNs are attractive due to their potential for efficient computation, but the non-differentiability of the quantization function makes it difficult to optimize them using traditional methods. To overcome this hurdle, the authors propose a novel approach that incorporates historical gradient information into the optimization process. They design a Historical Gradient Storage (HGS) module to model the sequence of past gradients and generate first-order momentum for optimization. Additionally, they introduce Fast and Slow Gradient Generation (FSG) to further improve gradient generation in hypernetworks. To produce more precise gradients, they also propose Layer Recognition Embeddings (LRE). The authors evaluate their method on the CIFAR-10 and CIFAR-100 datasets and demonstrate that it achieves faster convergence and lower loss values compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers work on a problem with Binary Neural Networks. These networks are special because they can be used in devices like smartphones or smart home appliances. The challenge is that the network gets stuck during training, so the authors develop new ways to help it learn better. They create two new tools: one that stores and uses past learning experiences, called Historical Gradient Storage, and another that helps generate gradients more accurately, called Fast and Slow Gradient Generation. They also add a third tool that makes the gradients even more precise, called Layer Recognition Embeddings. The authors test their methods on pictures and show that they work better than previous approaches. |
Keywords
» Artificial intelligence » Optimization » Quantization
