通过旋转轨道扭矩利用时间依赖的磁性纹理动力学,利用物理增强的神经形态计算.
Yifan Zhang1,2, Yu Li1,2, Huai Lin1,3
1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 11, 2025
概括
研究人员为神经形态计算开发了新的磁性纹理设备. 这些设备有效地同时执行视觉识别和组合优化任务,为先进的AI硬件铺平了道路.
科学领域:
- 神经形态计算是一种神经形态计算.
- 这就是Spintronics.
- 材料科学 是一种材料科学.
背景情况:
- 在单个平台上同时进行视觉识别和组合优化提供了效率和实时处理的好处.
- 硬件限制目前限制了多功能神经形态计算的进步.
研究的目的:
- 报告一种全电控制的新型迷宫磁纹理 (MT) 装置.
- 为了证明设备的同时视觉识别和组合优化能力.
- 为了克服当前神经形态计算平台的硬件限制.
主要方法:
- 利用旋转轨道扭矩 (SOT) 效应在室温下创建,操纵和检测可训练的MT设备.
- 通过动态导电矩阵调节非线性磁电阻,模仿生物突触可塑性.
- 在内存计算任务中使用了带有 SOT-MT 设备的交叉条数组.
主要成果:
- 在MNIST上实现了超过93%的测试准确度,用于模式识别.
- 在使用霍普菲尔德网络解决八个城市的旅行销售员问题时,成功率超过95%.
- 在室温下证明SOT-MT设备的高效和可靠运行.
结论:
- 开发的SOT-MT设备显示出各种内存计算任务的巨大潜力.
- 波动的协同调整提高了这些神经形态设备的性能.
- 这项工作推进了动态网络MT设备,使单个神经形态硬件上的认知和优化能够高效融合.
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