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基于三角尖的神经形态信号处理系统

Shuai Wang¹, Dehao Zhang¹, Ammar Belatreche²

¹Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural networks : the official journal of the International Neural Network Society

|March 13, 2025

概括

本研究介绍了一种新的神经形态信号处理系统,使用尖端神经网络 (SNN) 和量子化. 该系统实现了最先进的性能,显著降低了内存和能源消耗,以高效地部署边缘设备.

科学领域:

神经形态工程的神经形态工程
信号处理信号处理
人工智能的人工智能

背景情况:

深度神经网络 (DNN) 在信号处理方面提供了高性能,但需要大量的计算资源,限制了边缘设备的应用.
边缘设备的资源限制需要节能和轻量化处理解决方案.

研究的目的:

为资源有限的边缘设备开发一种节能,轻量级的神经形态信号处理系统.
为了提高效率,利用尖端神经网络 (SNN) 和量子化技术.

主要方法:

开发了一种值适应编码 (TAE) 方法,将模拟信号转换为稀疏的三元尖峰列车,减少能量和内存.
引入了与TAE兼容的量化三元SNN (QT-SNN),量化膜潜力和突触重量以减少记忆.
评估了系统的语音和脑电图 (EEG) 识别任务.

主要成果:

在信号处理任务中实现了最先进的 (SOTA) 性能.
与现有方法相比,显示了94%的内存需求减少.
通过理论分析展示了比其他SNN方法大7.5倍的节能效果.

结论:

拟议的神经形态系统为边缘设备的信号处理提供了高效和有效的解决方案.
TAE和QT-SNN的组合显著降低了内存和能量需求,同时保持了高性能.
这项工作为在现实世界应用中节能信号处理提供了一个有前途的方向.

关键词:

关键字发现和EEG.神经对信号进行编码.神经信号处理神经信号处理.量子化激增神经网络的神经网络.三级尖端神经网络的神经网络.

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