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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
Published on: August 9, 2024
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Efficient speech command recognition leveraging spiking neural networks and progressive time-scaled curriculum
Jiaqi Wang1, Liutao Yu2, Liwei Huang3
1Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.
Summary
Spiking neural networks (SNNs) can now process long temporal patterns efficiently on edge devices. Our SpikeSCR framework and PTCD method reduce energy use by 54.8% while maintaining high performance in speech command recognition.
Area of Science:
- Neuromorphic Computing
- Artificial Intelligence
Background:
- Spiking neural networks (SNNs) excel at temporal information processing due to their event-driven nature.
- High performance in SNNs often requires large time steps, increasing deployment burdens for edge computing.
- Balancing performance and energy efficiency is crucial for temporal pattern detection in edge devices.
Purpose of the Study:
- To develop a high-performance, low-energy framework for temporal pattern detection in edge devices using SNNs.
- To address the trade-off between performance and energy consumption in SNNs for edge applications.
Main Methods:
- Proposed SpikeSCR: a fully spike-driven framework with a global-local hybrid structure for efficient representation learning and long-term capabilities.
- Introduced Progressive Time-Scaled Curriculum Distillation (PTCD) to reduce time steps and energy consumption through progressive knowledge transfer.
- Evaluated on Spiking Heidelberg Dataset (SHD), Spiking Speech Commands (SSC), and Google Speech Commands (GSC) V2.
Main Results:
- SpikeSCR outperformed state-of-the-art SNNs on three benchmark datasets with identical time steps.
- PTCD reduced time steps by 60% and energy consumption by 54.8% while preserving comparable performance.
- Achieved a significant reduction in deployment burden for edge computing applications.
Conclusions:
- The SpikeSCR framework and PTCD method offer an effective solution for high-performance, low-energy temporal processing in edge neuromorphic systems.
- This work provides valuable insights for tackling long time-sequence processing challenges in resource-constrained environments.
- The proposed methods enable efficient and powerful SNNs for real-world edge AI applications.
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