对于自旋式人工神经网络的认知准确度的双方式改变
Anuj Kumar1, Debasis Das2, Dennis J X Lin3
1Physics Department, National University of Singapore, Singapore, 117551, Singapore.
Nanoscale horizons
|July 2, 2024
概括
这项研究增强了使用磁道连接 (MTJ) 的自旋式人工神经网络 (ANN),具有可调道磁电阻 (TMR) 和多个状态. 这种方法提高了复杂的人工智能任务的准确性,为先进的神经形态计算铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 基于Spintronics的ANN为人工智能和机器学习提供非挥发性,快速和节能计算.
- 以前用于ANN的多态设备概念由于低磁阻而损害了准确性.
研究的目的:
- 为了提高全旋转人工神经网络 (ANN) 的认知性能.
- 通过调整道磁阻 (TMR) 和复合磁道连接 (MTJ) 中的状态数量来实现这一目标.
主要方法:
- 通过调整复合MTJ中的自由层厚度来控制TMR变化 (33-78%).
- 操纵了抵抗状态的数量 (对于n=1-3个细胞的n+1个状态),状态之间的TMR差异至少为21%.
- 使用MNIST手写数字和时尚对象数据库评估ANN性能.
主要成果:
- 测试准确性随着固定自由层厚度或TMR的更多中间状态而增加.
- 单细胞MTJ精度与TMR线性改善 (数字8.3%,时尚物品7.4%).
- 复杂的TMR依赖性观察到与增加的突触复杂性在2和3细胞MTJs.
结论:
- 多级电阻和TMR的双模调节增强了自旋式ANN的认知性能.
- 为先进的内存和神经形态计算应用建立了可行的途径.
- 展示了更准确和更有效的AI硬件的潜力.
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