Summary of Short-long Convolutions Help Hardware-efficient Linear Attention to Focus on Long Sequences, by Zicheng Liu et al.
Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences
by Zicheng Liu, Siyuan Li, Li Wang, Zedong Wang, Yunfan Liu, Stan Z. Li
First submitted to arxiv on: 12 Jun 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 paper proposes a hybrid model that combines linear attention with state space models (SSMs) to process long sequences efficiently. Linear attention utilizes computation tricks to achieve linear complexity, while SSMs use non-data-dependent memory patterns to emphasize near and neglect distant information. The authors address the issues of hardware-efficient implementation for linear attention and stabilization of SSMs by proposing CHELA, a model that replaces SSMs with short-long convolutions and implements linear attention using a divide-and-conquer approach. The model maintains real linear complexity while enjoying global abstraction and data-dependent selection from stable SSMs and linear attention. Experimental results on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers process long sequences faster by combining two ideas: one that uses a special kind of attention, and another that helps computers remember things they saw before. The first idea is called linear attention, and it makes computations faster by being more efficient. The second idea is about how computers can use memories from the past to help them make decisions now. The authors want to make these ideas work together better, so they come up with a new model that combines both. They test this model on some big datasets and show that it works well. |
Keywords
» Artificial intelligence » Attention
