在用于传感器内计算的视网形硬件中的合铁电-光子记忆
Ngoc Thanh Duong1, Yufei Shi1, Sifan Li1
1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 18, 2024
概括
这项研究介绍了一种使用2Dα-In2Se3材料的新型人工视觉传感器. 该设备模仿人类视网膜以实现高效的图像处理和分类,在MNIST数据集上达到94%的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 推动了对智能传感器中节能,实时处理的需求.
- 传感器内计算,将计算放在数据源,对于边缘处理至关重要.
- 现有的人工视觉系统试图模拟生物视觉,以提高性能.
研究的目的:
- 开发一个原型的视觉传感器,整合光敏感性和铁电.
- 模仿人类视网膜功能,如摄影感应和记忆计算.
- 用新材料展示传感器内图像处理能力.
主要方法:
- 使用了二维 (2D) α-In2Se3 材料,具有集成的铁电和光敏感性.
- 设计了一种模仿视网膜光受体和亚马克林细胞的装置,使用电开关极化.
- 通过具有预编程内核的光电晶体管网络实现了传感器内卷积图像处理.
主要成果:
- 通过使用光子诱导的短期可塑性,在编码和分类12,000张MNIST图像中获得了≈94%的准确性.
- 展示了用于图像分析的门调节式激发和抑制功能.
- 成功执行传感器内卷积,用于图像中的边缘和特征增强.
结论:
- 铁电 α-In2Se3 显示了对紧和高效的视网形硬件的巨大潜力.
- 开发的设备为先进的人工视觉系统提供了一个有前途的平台.
- 这种方法推进了用于实时视觉数据处理的传感器内计算.
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