In this paper, the intrinsic physical characteristics of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) devices are leveraged to realize sigmoidal neurons in neuromorphic architectures. Performance comparisons with the previous power- and area-efficient sigmoidal neuron circuits exhibit 74x and 12x reduction in power-area-product values for the proposed SOT-MRAM based neuron. To verify the functionally of the proposed neuron within larger scale designs, we have implemented a circuit realization of a 784x16x10 SOT-MRAM based multiplayer perceptron (MLP) for MNIST pattern recognition application using SPICE circuit simulation tool. The results obtained exhibit that the proposed SOT-MRAM based MLP can achieve accuracies comparable to an ideal binarized MLP architecture implemented on GPU, while realizing orders of magnitude increase in processing speed.

中文翻译：

---

用于神经形态架构的基于 SOT-MRAM 的 Sigmoidal 神经元

在本文中，利用自旋轨道扭矩 (SOT) 磁阻随机存取存储器 (MRAM) 设备的内在物理特性来实现神经形态架构中的 sigmoidal 神经元。与之前的功率和面积效率高的 sigmoidal 神经元电路的性能比较显示，所提出的基于 SOT-MRAM 的神经元的功率面积乘积值降低了 74 倍和 12 倍。为了在更大规模的设计中验证所提出的神经元的功能，我们使用 SPICE 电路仿真工具实现了基于 784x16x10 SOT-MRAM 的多人感知器 (MLP) 的电路实现，用于 MNIST 模式识别应用。获得的结果表明，所提出的基于 SOT-MRAM 的 MLP 可以达到与在 GPU 上实现的理想二值化 MLP 架构相当的精度，