Summary of Adapmoe: Adaptive Sensitivity-based Expert Gating and Management For Efficient Moe Inference, by Shuzhang Zhong et al.
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
by Shuzhang Zhong, Ling Liang, Yuan Wang, Runsheng Wang, Ru Huang, Meng Li
First submitted to arxiv on: 19 Aug 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 The proposed AdapMoE framework addresses the efficiency challenges of deploying Mixture-of-Experts (MoE) models on edge devices. By introducing adaptive expert gating and management, AdapMoE reduces on-demand loading overheads. The sensitivity-based strategy adjusts the number of activated experts dynamically based on layer and token heterogeneity. Additionally, prefetching and cache management techniques are integrated to further reduce loading latency. Comprehensive evaluations demonstrate a 25% reduction in activated experts and a 1.35x speedup without accuracy degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AdapMoE is a new way to make language models work better on small devices like phones or smart speakers. It helps Mixture-of-Experts (MoE) models use less power by reducing the amount of information that needs to be loaded when needed. The algorithm adjusts how many “experts” are used based on what’s being done, and also uses caching and prefetching to make things faster. |
Keywords
» Artificial intelligence » Mixture of experts » Token
