Summary of Demo: Decoupled Momentum Optimization, by Bowen Peng et al.
DeMo: Decoupled Momentum Optimization
by Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 research paper introduces DeCoupled Momentum (DeMo), a novel optimizer and data parallel algorithm that reduces communication requirements between accelerators during neural network training. By decoupling momentum updates, the authors show that synchronizing full optimizer states and model parameters is unnecessary, achieving improved convergence compared to state-of-the-art optimizers like AdamW. DeMo eliminates the need for high-speed interconnects when pre-training large-scale foundation models, making it topology-agnostic and architecture-independent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make neural networks train faster by letting each part of a network work separately. Instead of sharing information all the time, they let each part do its own thing and then bring everything together later. This makes training faster and uses less energy. The new way is called DeCoupled Momentum (DeMo) and it works well with big neural networks on different kinds of hardware. |
Keywords
» Artificial intelligence » Neural network
