大脑启发的储水器计算与动态记忆器用于视听情绪识别
Sicheng Wan1,2, Yibo Wang1, Honglei Chen2
1Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, China.
ACS applied materials & interfaces
|January 26, 2026
概括
这项研究引入了一种新的脑启发的计算框架,使用动态记忆器进行高效的视听情绪识别. 该系统在低能耗的情况下实现了高精度,推进了尖端人工智能 (AI) 能力.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 边缘人工智能和多式联接感知系统面临着由于·诺伊曼架构的局限性而导致的计算瓶和高培训成本.
- 新兴的设备技术和新的计算架构对于提高效率和降低人工感知中的能源消耗至关重要.
研究的目的:
- 开发一个由大脑启发的储库计算 (RC) 框架,使用动态memristors进行高效的视听情感识别.
- 为解决边缘AI应用程序当前架构的局限性.
主要方法:
- 使用了Ag/WOx/ITO动态记忆器,表现出生物突触功能作为物理储存器.
- 采用了memristor的短暂响应和非线性映射来将时空视听信号转换为可分离的高维储存状态,使用4位脉冲序列.
主要成果:
- 实现了基于语音的情感识别准确率为95.46%,面部表情识别准确率为91.08%.
- 证明了每脉冲运行大约3.9nJ的超低能耗.
结论:
- 开发的基于memristor的RC框架为边缘设备的低功耗,实时视听感知提供了可行的硬件范式.
- 这种方法为先进的神经形态计算和更高效的人工感知系统铺平了道路.
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