论文标题
电压门控域壁磁性隧道连接点基于尖峰卷积神经网络
Voltage Gated Domain Wall Magnetic Tunnel Junction-based Spiking Convolutional Neural Network
论文作者
论文摘要
我们提出了一种新型的自旋轨道扭矩（SOT）驱动和电压门控域壁运动（DWM）的MTJ设备及其在神经形态计算中的应用。我们表明，通过利用DWM的电压控制门控效果，可以消除访问晶体管。该设备对单个突触写作提供了更多的控制，并显示了高度线性的突触行为。评估对诸如DMI和温度等材料参数的线性依赖性，以进行实现环境性能分析。此外，使用基于天际的泄漏集成和火神经元模型，我们在CIFAR-10数据集上实施了尖峰卷积神经网络，以用于模式识别应用。该设备的准确性高于85％，证明了其在SNN中的适用性。
We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion (DWM)-based MTJ device and its application in neuromorphic computing. We show that by utilizing the voltage-controlled gating effect on the DWM, the access transistor can be eliminated. The device provides more control over individual synapse writing and shows highly linear synaptic behavior. The linearity dependence on material parameters such as DMI and temperature is evaluated for real-environment performance analysis. Furthermore, using skyrmion-based leaky integrate and fire neuron model, we implement the spiking convolutional neural network for pattern recognition applications on the CIFAR-10 data set. The accuracy of the device is above 85%, proving its applicability in SNN.
