Summary of A Survey on Inference Optimization Techniques For Mixture Of Experts Models, by Jiacheng Liu et al.
A Survey on Inference Optimization Techniques for Mixture of Experts Models
by Jiacheng Liu, Peng Tang, Wenfeng Wang, Yuhang Ren, Xiaofeng Hou, Pheng-Ann Heng, Minyi Guo, Chao Li
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 presents an in-depth survey of optimization techniques for large-scale Mixture of Experts (MoE) models, which have shown promise in artificial intelligence. MoE models offer enhanced capacity and efficiency through conditional computation, but deploying them poses significant challenges regarding computational resources, latency, and energy efficiency. The authors categorize optimization approaches into model-level, system-level, and hardware-level optimizations, exploring architectural innovations like efficient expert design, attention mechanisms, compression techniques, algorithm improvements, distributed computing, load balancing, and scheduling algorithms. Hardware-specific optimizations and co-design strategies are also discussed to maximize throughput and energy efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models can do many things really well, but they’re hard to use because they need lots of computer power, take a long time to process information, and use a lot of energy. To make them more useful, researchers have come up with ways to optimize MoE models for better performance. The paper looks at all these different methods, grouping them into three categories: what you can do with the model itself, how you can run it on a computer, and special tricks to make it work faster and use less energy. By understanding how to make MoE models more efficient, researchers hope to create new AI applications that are more powerful and easier to use. |
Keywords
» Artificial intelligence » Attention » Mixture of experts » Optimization
