基于物理计算的模拟语音识别
Mohamadreza Zolfagharinejad1, Julian Büchel2, Lorenzo Cassola1,3
1NanoElectronics Group, MESA+ Institute and BRAINS Center for Brain-Inspired Computing, University of Twente, Enschede, the Netherlands.
Nature
|September 17, 2025
概括
我们开发了一种高效的边缘时间信号处理器, 该系统可实现低功耗的语音命令的高精度,从而实现先进的边缘人工智能应用.
科学领域:
- 计算机科学
- 材料科学
- 电气工程
背景情况:
- 边缘设备中的分散计算 (物联网,自动驾驶,医疗保健) 需要高效,低功耗的时间依赖信号处理.
- 传统处理器面临由于·诺伊曼瓶和域转换的局限性,阻碍了边缘系统的性能.
- 在材料内计算提供了一种通过在材料本身进行计算来克服这些局限性的新方法.
研究的目的:
- 提出并演示一个边缘时间信号处理器,利用物质计算来有效地提取和分类特征.
- 在基准语音数据集上实现接近软件的准确性,并显著减少延迟和功耗.
- 推进紧,高效和高性能异质智能边缘处理器的开发.
主要方法:
- 开发了一种非线性,可在室温下重新配置的非线性处理单元层,用于从原始音频信号中提取模拟时间域特征.
- 实现了一个紧的模拟内存计算芯片,用于神经网络分类.
- 在TI-46-Word和谷歌语音命令数据集上训练和评估系统.
主要成果:
- 在基准数据集上实现语音命令识别的近软件精度.
- 证明了整个处理管道的次毫秒延迟.
- 报告的低能耗:约300nJ用于特征提取和约78μJ用于分类 (潜在下降至约10μJ).
结论:
- 拟议的基于物质计算的边缘时间信号处理器为高效边缘人工智能提供了一个有前途的解决方案.
- 系统的高精度,低延迟和最低功耗为先进的,异质的智能边缘设备铺平了道路.
- 这项工作突显了物质计算在时间依赖信号的边缘处理方面的潜力.
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