一个40纳米的3.9mW,200words/Min神经信号处理器在语音解码中用于大脑机器接口
IEEE transactions on biomedical circuits and systems
|November 13, 2025
概括
这项研究引入了用于脑机界面 (BMI) 的新型神经信号处理器,显著提高了语音解码效率和速度. 处理器减少了能源消耗和内存大小,为用户提供更快,更准确的通信.
科学领域:
- 神经科学是一个神经科学.
- 计算机工程 计算机工程
- 生物医学工程 生物医学工程
背景情况:
- 大脑机器接口 (BMI) 技术促进了人类大脑与外部设备之间的直接通信.
- 实时语音解码是BMI的关键应用程序,使严重运动障碍的人能够进行沟通.
研究的目的:
- 开发一种高效的神经信号处理器,用于大脑机器接口中的实时语音解码.
- 为了优化能源消耗,内存足迹和神经信号处理的处理延迟.
主要方法:
- 实现了语音尝试检测,以减少能源消耗和通道要求.
- 使用稀疏编码和混合精度算术进行重量编码,减少内存大小.
- 使用计算重新排序,部分总和缓存,并利用处理元件 (PE) 阵列中的输入/重量稀疏性来降低延迟.
- 在光束搜索引擎中引入了一个近似的top-k选择架构.
主要成果:
- 实现了46%的能源消耗降低,并将语音尝试检测的频道从128减少到16个.
- 通过优化编码和计算,神经网络离芯片内存大小减少了80%,处理延迟减少了55%.
- PE阵列稀疏性利用降低了95%的延迟,混合精度乘法器减少了27%的面积.
- 在语音解码方面实现了 16.6% 的电话错误率和 23.5% 的单词错误率.
- 展示了200字/分钟的最大通信速度,比最先进的速度快16.7至42.6倍,解码高达125,000个单词.
结论:
- 拟议的神经信号处理器显著提升了语音解码的实时BMI能力.
- 在能量,内存和延迟方面的优化使得解码速度和容量前所未有的.
- 这项技术有望改善语言和运动障碍患者的沟通可访问性.
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