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基于STDP设计的人工神经元和突触的实现,由MTJ设备设计

Manman Wang¹, Yuhai Yuan¹, Yanfeng Jiang¹

¹Department of Electrical Engineering, School of Internet of Things (IoTs), Jiangnan University, Wuxi 214122, China.

|October 28, 2023

概括

这项研究介绍了一种新的尖端神经网络 (SNN),使用旋转转矩磁道连接 (STT-MTJ) 装置,用于神经元和突触. 这种旋转式方法证明了有效的手写数字识别,推进了神经形态计算.

科学领域:

神经形态计算是一种神经形态计算.
这就是Spintronics.
人工智能的人工智能

背景情况:

尖端神经网络 (SNN) 是高级AI模型,模仿大脑功能.
突触可塑性,就像尖峰时间依赖可塑性 (STDP),对于SNN学习至关重要.
对于SNN来说,Spintronic设备提供了一个有希望的硬件平台.

研究的目的:

提出使用旋转转矩磁道连接 (STT-MTJ) 装置的新型SNN架构.
使用STT-MTJ实现神经元和突触功能.
为了验证SNN在现实世界的任务上的表现.

主要方法:

模仿SNN神经元和突触与STT-MTJ设备.
通过MTJ导电来表示突触重量.
使用随机兰道-利夫希茨-吉尔伯特 (LLG) 方程建模神经元尖端,以捕捉热噪声和随机切换行为.

主要成果:

使用STT-MTJ设备成功模拟了一个简化的SNN.
MTJ的导电性代表了突触权重.
拟议的SNN通过在MNIST数据库上的手写数字识别任务实现了功能验证.

结论:

STT-MTJ设备可以有效模拟SNN组件,从而实现一个旋转型神经形态计算范式.
螺旋电子与SNNs的整合为高效和类似大脑的计算提供了一条途径.
这项工作验证了基于STT-MTJ的SNN在人工智能应用 (如模式识别) 中的潜力.

关键词:

在STT-MTJJ.图像识别功能图像识别功能神经元神经元是一个神经元.突触突触是指突触中的突触.

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