Summary of Continual Deep Learning on the Edge Via Stochastic Local Competition Among Subnetworks, by Theodoros Christophides and Kyriakos Tolias and Sotirios Chatzis
Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks
by Theodoros Christophides, Kyriakos Tolias, Sotirios Chatzis
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 a novel method that utilizes stochastic competition principles to promote sparsity in deep networks, reducing memory footprint and computational demand. The proposed architecture consists of blocks of units that compete locally to win the representation of each new task, resulting in sparse task-specific representations from each network layer. This approach also facilitates training on edge devices by sparsifying both weights and weight gradients. During inference, the network retains only the winning unit and zeros out all non-winning unit weights for the specific task, making it an efficient and scalable solution for continual learning in resource-limited environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make deep learning work better on small devices like phones or smart home devices. It uses a special type of competition between different parts of the network to make sure that only the most important information is stored. This makes it possible to train and use these networks even when there are strict limits on how much memory or computing power is available. The method also helps reduce the amount of data needed for training, making it more efficient overall. |
Keywords
» Artificial intelligence » Continual learning » Deep learning » Inference
