混合时钟驱动的自旋神经元器件激活特性和计算性能

Activation function and computing performance of spin neuron driven by magnetic field and strain

自旋神经元是一种新兴的人工神经形态器件,其具有超低功耗、强非线性、高集成度和存算一体等优点,是构建新一代神经网络的强有力候选者.本文提出了一种磁场辅助磁弹应变驱动的混合时钟自旋神经元,利用OOMMF微磁学仿真软件建立了该神经元器件的微磁学模型,基于LLG方程建立了其数值仿真模型,利用所设计的自旋神经元构建了3层神经网络,研究了不同纳磁体材料(Terfenol-D,FeGa,Ni)神经元器件的激活特性及其对MNIST手写数字数据集识别性能的影响.OOMMF仿真和数值模拟发现,设计的混合时钟结构能够成功驱动纳磁体发生随机磁化翻转,有效模拟生物神经元的激活行为和特性.MNIST手写数字识别结果表明:当输入不同范围的磁场使得3种材料的自旋神经元都达到饱和识别精度时,该自旋神经元器件具有与Sigmoid神经元器件相同的识别能力,有望替代传统的CMOS神经元,并且选择合适的磁致伸缩层材料能够进一步降低智能计算的整体功耗;当输入相同范围的磁场时,Ni构成的自旋神经元的识别速度较慢.研究结果可为新型人工神经网络和智能电路的设计及应用奠定一定的理论基础.

The spin neuron is an emerging artificial neural device which has many advantages such as ultra-low power consumption,strong nonlinearity,and high integration.Besides,it has ability to remember and calculate at the same time.So it is seen as a suitable and excellent candidate for the new generation of neural network.In this paper,a spin neuron driven by magnetic field and strain is proposed.The micromagnetic model of the device is realized by using the OOMMF micromagnetic simulation software,and the numerical model of the device is also established by using the LLG equation.More importantly,a three-layer neural network is composed of spin neurons constructed respectively using three materials(Terfenol-D,FeGa,Ni).It is used to study the activation functions and the ability to recognize the MNIST handwritten datasets.c Results show that the spin neuron can successfully achieve the random magnetization switching to simulate the activation behavior of the biological neuron.Moreover,the results show that if the ranges of the inputting magnetic fields are different,the three materials’neurons can all reach the saturation accuracy.It is expected to replace the traditional CMOS neuron.And the overall power consumption of intelligent computing can be further reduced by using appropriate materials.If we input the magnetic fields in the same range,the recognition speed of the spin neuron made of Ni is the slowest in the three materials.The results can establish a theoretical foundation for the design and the applications of the new artificial neural networks and the intelligent circuits.