Summary of Spikingssms: Learning Long Sequences with Sparse and Parallel Spiking State Space Models, by Shuaijie Shen et al.
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
by Shuaijie Shen, Chao Wang, Renzhuo Huang, Yan Zhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang, Luziwei Leng
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); 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 introduces SpikingSSM, a novel approach to long sequence learning that leverages the strengths of both spiking neural networks (SNNs) and state space models (SSMs). By integrating SSM blocks with hierarchical neuronal dynamics inspired by dendritic neuron structure, SpikingSSM achieves competitive performance on benchmark tasks like long range arena while realizing significant sparsity (up to 90%) in network architecture. Additionally, the proposed approach demonstrates potential as a backbone architecture for low computation cost large language models (LLMs), surpassing existing SNN-based LLMs on the WikiText-103 dataset with a smaller model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn long sequences of information using neural networks. It combines two different approaches: spiking neural networks, which are good at processing temporal data, and state space models, which are good at learning patterns in data. The new approach, called SpikingSSM, works by integrating these two methods together. This allows it to learn long sequences quickly while still being efficient with its use of computer resources. In tests, SpikingSSM was able to perform as well as other approaches on some tasks and even outperformed them in others. |
