对于神经元设备的压力介导多态 skyrmion
Shengbin Shi1, Yunhong Zhao2, Jiajun Sun1
1Department of Engineering Mechanics, Zhejiang University, Zheda Road 38, Hangzhou, Zhejiang 310027, China. jw@zju.edu.cn.
Nanoscale
|May 28, 2024
概括
研究人员开发了一种基于skyrmion的新型人工神经元,用于节能的神经形态计算. 该设备使用电压控制模仿生物神经元,实现高级AI应用的高精度和低功耗.
科学领域:
- 这就是Spintronics.
- 神经形态计算是一种神经形态计算.
- 人工智能的人工智能
背景情况:
- 磁性 skyrmions 提供了神经形态计算的潜力,由于稳定性和纳米尺寸大小.
- 人工神经元中的电流驱动的 skyrmion 运动在深度网络的能源效率和集成密度方面面临挑战.
研究的目的:
- 为了呈现一个紧而节能的基于 skyrmion 的人工神经元.
- 使用电压操纵模仿生物神经元的整合和火 (IF) 功能.
主要方法:
- 使用铁磁/重金属/铁电层制造一个神经元神经元装置.
- 使用应变介导的电压控制来操纵skyrmion状态.
- 使用拟议的设备实现一个尖端神经网络 (SNN).
主要成果:
- 这种神经元成功地模仿了整合与火 (IF) 功能.
- 一个尖端神经网络 (SNN) 在MNIST数据集上实现了95.08%的准确性.
- 该设备的低功耗约为每个时代每个神经元46.8 fJ.
结论:
- 开发出的神经元神经元为节能和高密度的神经形态计算提供了一种新的方法.
- 电压控制的 skyrmion 操纵是人工神经元设计的一个可行的策略.
- 这项技术为更强大,更高效的人工智能系统铺平了道路.
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