论文标题
基于SOT-MRAM的Sigmoidal神经元用于神经形态架构
SOT-MRAM based Sigmoidal Neuron for Neuromorphic Architectures
论文作者
论文摘要
在本文中，借用了旋转轨道扭矩（SOT）磁磁性随机记忆（MRAM）设备的固有物理特性，以实现神经形态架构中的Sigmoidal神经元。与先前的功率和区域效力的乙状结肠神经元电路的性能比较表现为拟议的基于SOT-MRAM的神经元的功率区域产物值74x和12倍降低。为了验证较大规模设计中提出的神经元的功能，我们已经实现了使用Spice Cource Migulation工具的MNIST模式识别应用程序的基于784x16x10 SOT-MRAM的多人感知器（MLP）的电路实现。获得的结果表明，所提出的基于SOT-MRAM的MLP可以实现与GPU上实施的理想二进制MLP体系结构相当的精确度，同时实现了加工速度的数量级。
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.
