基于芯片上的基于memristor的系统上的融合网络实现的实时信号处理
Zixu Wang1, Wenhao Song1, Tong Wang1
1University of Southern California, Los Angeles, CA 90089, USA.
Science advances
|July 25, 2025
概括
基于memristor的系统使用内存计算进行实时信号处理. 这种融合网络结合了离散的里埃转换 (DFT) 和卷积神经网络 (CNN) 以实现高效的音频和视频分析.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- ·诺伊曼瓶增加了计算中的能源消耗,特别是数据密集型信号处理.
- 基于memristor的内存计算通过集成计算和内存提供了节能解决方案.
- 使用memristor的模拟计算为克服传统数字系统的局限性提供了一个有希望的途径.
研究的目的:
- 通过在芯片 (SoC) 上使用基于memristor的新型模拟系统来演示实时信号处理.
- 在单一芯片上集成实时离散里埃变换 (DFT) 和卷积神经网络 (CNN) 功能.
- 评估拟议的SoC用于信号处理任务的能源效率和性能.
主要方法:
- 一个128x128的memristor交叉杆阵列被用于模拟计算.
- 该系统实现了一个融合网络,将实时DFT和CNN结合起来进行信号分析.
- 该系统使用AudioMNIST数据集和视频边缘检测进行了音频分类测试.
主要成果:
- 基于memristor的DFT实现了音频信号的高峰信号噪声比为33.49dB.
- 集成的CNN在分类音频谱图方面显示了94.72%的准确性.
- 基于卷积的边缘检测成功应用于实时视频.
结论:
- 基于memristor的SoC可以通过在模拟内存中执行计算来实现高效的实时信号处理.
- 与传统数字系统相比,经过证明的DFT-CNN合并网络可以大大节省能源.
- 这项技术对未来的低功耗,高性能信号处理应用具有巨大的潜力.
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