Summary of Q-s5: Towards Quantized State Space Models, by Steven Abreu et al.
Q-S5: Towards Quantized State Space Models
by Steven Abreu, Jens E. Pedersen, Kade M. Heckel, Alessandro Pierro
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 paper explores the impact of quantization on State Space Models (SSMs), a promising alternative to transformers for sequence modeling. Specifically, it investigates the effects of quantization-aware training (QAT) and post-training quantization (PTQ) on the S5 model’s performance across various tasks, including dynamical systems modeling, sequential MNIST (sMNIST), and Long Range Arena (LRA). The results show that fully quantized S5 models can be deployed to edge platforms with minimal accuracy loss. However, recurrent weights below 8-bit precision significantly degrade performance on most tasks, while other components can be compressed further without significant loss of performance. Notably, PTQ only performs well on language-based LRA tasks, whereas QAT is required for all others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make a type of computer model called State Space Models (SSMs) work better and faster. They’re trying to find ways to use these models on devices that don’t have as much power or memory, like smartwatches or smartphones. The researchers tested different methods for making the models smaller and faster, and they found out what works best. They also discovered that some parts of the model can be made really small without losing its ability to do its job well. This information will help scientists develop better SSMs that can work on devices with limited resources. |
Keywords
» Artificial intelligence » Precision » Quantization
