Summary of Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation, by Jiaqi Wang et al.
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation
by Jiaqi Wang, Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhenxi Song, Min Zhang, Zhengyu Ma, Zhiguo Zhang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 abstract discusses the advantages of spiking neural networks (SNNs) in processing temporal information due to their embedded time sequences as time steps. Recent studies have demonstrated SNNs’ effectiveness in speech command recognition, but large time steps lead to increased deployment burdens for edge computing applications. To balance high performance and low energy consumption, the authors propose a fully spike-driven framework called SpikeSCR, which exhibits long-term learning capabilities with extended time steps. Additionally, an effective knowledge distillation method based on curriculum learning (KDCL) is proposed to further reduce power efficiency while maintaining high performance. The method is evaluated on three benchmark datasets, demonstrating that SpikeSCR outperforms current state-of-the-art methods across these datasets with the same time steps. Furthermore, KDCL reduces the number of time steps by 60% and energy consumption by 54.8% while maintaining comparable performance to recent SOTA results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spike neural networks are great at processing information that changes over time because they naturally use time sequences as “time steps”. This helps them recognize speech commands really well, but it can make them harder to use in devices like smartphones where battery life is important. The authors came up with a new way to build these networks called SpikeSCR that uses a combination of global and local structures to learn information quickly. They also developed a way to “distill” knowledge from one network to another, which helps reduce the energy needed to run the network while still getting good results. |
Keywords
* Artificial intelligence * Curriculum learning * Knowledge distillation
