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在线丝记忆器件中实现低功耗模拟电阻开关,以实现节能模拟内存计算

Jaehyun Kang^1,2, Sungmin Lee², Taeyoon Kim¹

¹Center for Semiconductor Technology, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.

Small (Weinheim an der Bergstrasse, Germany)

|September 15, 2025

概括

这项研究介绍了一种新的双矩阵记忆装置,用于节能模拟内存计算. 新设计在深度学习应用中显著降低了运行电压和功耗.

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:

传统的·诺伊曼架构面临能源低效,特别是在深度学习中.
丝状记忆器件在高工作电压和高功耗下扎.
模拟内存计算为能源效率提供了一个有希望的替代方案.

研究的目的:

设计一种新的双矩阵记忆装置,以克服现有的光纤记忆装置的局限性.
为了降低运行电压和模拟开关电流,以降低功耗.
在深度学习应用中证明拟议设备的有效性.

主要方法:

采用双矩阵丝状开关方法,采用GeSe2 (高流动性) 和密集无形 (低流动性) 矩阵.
嵌入式Ag和Pt纳米集群层,以提高设备性能.
在Spiking-VGG9架构上进行模拟,使用量子化意识训练和tiki-taka方法.

主要成果:

在成型和设置电压方面实现了50%以上的降低.
降低了超过96%的重置电流和93%的模拟能耗 (0.98 pJ).
经过证明的稳定保留 (≈24小时) 和耐力 (≈50k周期).
模拟显示CIFAR10的准确率为89.38%,CIFAR10-DVS的准确率为63.70%,总能耗减少了60.42%.

结论:

拟议的双矩阵内存设备显著提高了模拟内存计算的能源效率.
这种方法有效地解决了深度学习硬件中的功耗挑战.
这项技术显示出开发低功耗AI加速器的巨大潜力.

关键词:

模拟内存计算中的模拟内存计算.模拟记忆器模拟记忆器丝状电阻切换机制的切换机制低功率模拟开关的低功率模拟开关刺激神经网络的神经网络.

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