Summary of Toward Large-scale Spiking Neural Networks: a Comprehensive Survey and Future Directions, by Yangfan Hu et al.
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
by Yangfan Hu, Qian Zheng, Guoqi Li, Huajin Tang, Gang Pan
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 In this paper, researchers explore the potential of energy-efficient large-scale neural networks inspired by the human brain’s spiking neural networks (SNNs). The rise of large language models has led to a surge in demand for computing resources and energy consumption, prompting the search for more efficient alternatives. The authors provide a survey of existing methods for developing deep SNNs, with a focus on emerging Spiking Transformers. They categorize learning methods into ANN-to-SNN conversion and direct training with surrogate gradients, and network architectures into DCNNs and Transformer architecture. A comprehensive comparison of state-of-the-art deep SNNs is also presented, highlighting the potential of large-scale SNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has made huge progress in AI areas like computer vision, speech recognition, and language processing. To make these advancements more energy-efficient, researchers are looking into spiking neural networks (SNNs) that work like our brains. The authors of this paper look at different ways to build deep SNNs, focusing on a new type called Spiking Transformers. They also compare the best ideas so far and suggest where future research should go. |
Keywords
» Artificial intelligence » Deep learning » Prompting » Transformer
