基于电压控制的概率切换磁道连接 (MTJ) 设备的静态神经形态计算架构
Liang Gao1, Chenxi Wang2, Yanfeng Jiang1
1School of Integrated Circuits, Jiangnan University, Wuxi 214122, China.
Micromachines
|February 27, 2026
概括
这项研究探讨了用于高效计算的自旋电子设备. 通过将电压控制的磁性异构 (VCMA) 和磁性道连接 (MTJ) 中的旋转厅效应 (SHE) 结合起来,研究人员降低了电力消耗,并为AI应用程序启用了随机计算.
科学领域:
- 这就是Spintronics.
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
背景情况:
- 集成电路在功率,面积和稳定性方面面临限制.
- 螺旋电子设备为克服传统计算范式提供了一条道路.
- 螺旋电子设备与计算架构的新型集成是必不可少的.
研究的目的:
- 通过使用电压控制的磁性异构 (VCMA) 和旋转厅效应 (SHE) 调查磁性道连接点 (MTJ) 的开关机制.
- 分析VCMA辅助切换SHE-MTJ设备的动态特性.
- 为随机和神经形态计算架构开发基础.
主要方法:
- 建立了一个基于兰道-利夫希茨-吉尔伯特 (LLG) 方程的宏旋近似模型.
- 研究了VCMA和SHE对MTJ切换的协同效应.
- 整合了一个热波动场,以实现随机切换行为.
主要成果:
- VCMA电压脉冲显著降低了所需的旋转霍尔电流密度和脉冲宽度,最大限度地减少了欧姆损失和朱尔加热.
- 经过证明的电压控制的SHE-MTJ设备表现出随机切换与西格形电压概率响应.
- 为二元化卷积神经网络 (CNN) 提出并设计了一个内存计算架构.
结论:
- 该研究提供了一个可行的spintronic实现低功耗,高能效边缘智能芯片.
- 开发的架构在CIFAR-10上使用SqueezeNet,仅使用125万个参数,实现了72.49%的Top-1准确度.
- 这项工作为基于spintronic的先进计算奠定了基础,特别是用于AI和神经形态应用.
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