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Updated: Jul 1, 2025

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通过发光记忆器实现跨层传输,用于构建具有传输学习能力的超深度神经网络

Zhenjia Chen^1,2, Zhenyuan Lin^1,2, Ji Yang³

¹Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China.

Nature communications

|March 2, 2024

概括

这项研究引入了新的硬件神经网络,用于超深度网络的发光记忆器,使人工智能处理速度更快,并提高了像图像识别和恢复等复杂任务的精度.

科学领域:

材料科学材料科学材料科学
计算机工程计算机工程
人工智能的人工智能

背景情况:

深度神经网络 (DNN) 是人工智能的基础,但由于深度限制,在硬件中实施具有挑战性.
现有的硬件神经网络主要集中在浅层架构上,面临长时间训练和信号干扰等问题.
梯度问题 (爆炸/消失) 阻碍了硬件实现中的深度网络的准确性.

研究的目的:

为了克服超深神经网络的硬件实现挑战.
为了使光学跨层传输的电信息使用新的memristor设备.
为边缘AI应用开发高精度,多功能硬件神经网络.

主要方法:

利用负紫外线光导发光发射记忆器来实现内在平行.
实施硬件和软件的共同设计,用于从电到光的信息传输.
拟议的混合超深光电和超分辨率重建神经网络 (54层和135层).

主要成果:

实现了光学跨层传输,克服了层次硬件限制.
开发了超深层网络 (54层和135层),表现出卓越的性能.
启用转移学习,在识别和恢复任务中匹配或超过基于软件的网络.

结论:

拟议的战略显示了高精度多功能硬件神经网络的巨大潜力.
发光的memristors和跨层设计是推进超深硬件AI的关键.
这些进步为更强大的前沿人工智能解决方案铺平了道路.

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