Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation. Recent research has demonstrated memristors and spintronic devices in various neural network designs boost efficiency and speed. This paper presents a biologically inspired fully spintronic neuron used in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the results are compared against those of current Hopfield neuromorphic architectures which use emerging technologies.

翻译：神经形态计算系统克服了传统的 von Neumann 计算结构的局限性。 这些计算系统可以通过使用比 CMOS 更高效的新兴技术进行神经计算来进一步改进。 最近的研究表明,在各种神经网络的设计中,有分子和脊椎装置提高了效率和速度。 本文展示了在完全脊柱式Hoptronic Hopfield RNN 中使用的、具有生物启发力的全脊椎神经元。 网络被用于解决任务,其结果与目前使用新兴技术的Hopfield神经形态结构相比较。