基于skyrmion的超低能耗学习自动元件用于自适应边缘智能
Kishore C1,2, Santhosh Sivasubramani1, Sarwath Sara C3
1Advanced Embedded Systems and IC Design Laboratory, Department of Electrical Engineering, Indian Institute of Technology (IIT) Hyderabad, Hyderabad 502284, India.
Nanotechnology
|November 3, 2025
概括
这项研究介绍了一种新的学习自动机,使用磁性 skyrmions 进行节能计算. 该设备通过控制状态转换的 skyrmion 运动来实现更快,低功耗的决策.
科学领域:
- 这就是Spintronics.
- 神经形态计算是一种神经形态计算.
- 机器学习硬件 机器学习硬件
背景情况:
- 磁性skyrmions是稳定的,纳米级的旋转纹理是低功耗计算的理想选择.
- 学习自动机需要高效的硬件来进行自适应性决策.
- 将学习自动机与内存设备集成,可以降低功耗.
研究的目的:
- 实施基于skyrmion的学习自动机,以节能,内存学习.
- 使用 skyrmion motion 演示一个硬件效率高的逻辑学习机制.
- 建立面向边缘的机器智能的可扩展的构建块.
主要方法:
- 使用微磁模拟 (MuMax3) 与一个Co/Pt双层.
- 在纳米尺度的轨道上绘制了有限态过渡到斯基米翁运动的映射.
- 通过优化电流密度驱动的 skyrmion 状态转换使用了旋转轨道扭矩.
主要成果:
- 实现了稳定的 skyrmion 核,运动和检测.
- 展示了一个基于skyrmion的 Tsetlin 机器元素,用于可解释的逻辑学习.
- 每个状态更新报告的超低能耗为4.86JJ.
- 观察到一个快速的5ns过渡时间,为skyrmion运动.
结论:
- 斯基米翁运动为实现自动学习提供了一个可行的机制.
- 与现有方法相比,拟议的装置提供了显著的能源减少 (99%).
- 这项工作为未来的边缘AI硬件提供了一个可扩展和可重新配置的平台.
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