有效的语音命令识别利用尖端的神经网络和渐进的时间尺度课程蒸
Jiaqi Wang1, Liutao Yu2, Liwei Huang3
1Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.
概括
尖端神经网络 (SNN) 现在可以在边缘设备上高效地处理长时间模式. 我们的SpikeSCR框架和PTCD方法可以减少54.8%的能源消耗,同时在语音命令识别方面保持高性能.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能的人工智能
背景情况:
- 尖端神经网络 (SNN) 卓越于时间信息处理,因为它们的事件驱动性.
- 在SNN中高性能通常需要很大的时间步骤,增加边缘计算的部署负担.
- 平衡性能和能源效率对于边缘设备的时间模式检测至关重要.
研究的目的:
- 开发一种高性能,低能耗的框架,用于使用SNN的边缘设备中的时间模式检测.
- 解决边缘应用的SNN中性能和能耗之间的权衡问题.
主要方法:
- 拟议的SpikeSCR:一个完全由尖端驱动的框架,具有全球-本地混合结构,用于高效的代表学习和长期能力.
- 引入逐步时间缩放课程蒸 (PTCD),通过逐步的知识转移来减少时间步骤和能源消耗.
- 在Spiking海德堡数据集 (SHD),Spiking语音命令 (SSC) 和谷歌语音命令 (GSC) V2.2上进行评估.
主要成果:
- 在三个具有相同时间步骤的基准数据集上,SpikeSCR的表现优于最先进的SNN.
- 在保持可比性能的同时,PTCD减少了60%的步骤时间和54.8%的能源消耗.
- 实现了对边缘计算应用程序的部署负担的显著降低.
结论:
- 在边缘神经形态系统中,SpikeSCR框架和PTCD方法为高性能,低能耗的时间处理提供了有效的解决方案.
- 这项工作为解决资源有限环境中长时间序列处理挑战提供了宝贵的见解.
- 提出的方法可以为现实世界边缘AI应用程序提供高效和强大的SNN.
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