scientific report|一种可用于脉冲神经网络片上STDP计算的磁性隧道结长短时随机突触 ...

脉冲神经网络已经成为了神经形态计算领域执行分类与识别任务的范式，然而CMOS技术作为其通用计算实现平台和硬件基础，难以与人脑的计算效率竞争。因此需要一种可以实现高效脉冲神经网络的纳米电子器件，普渡大学的Gopalakrishnan Srinivasan等提出了一种基于磁性隧道结与重金属层的双层随机突触。基于相互连接的神经元之间的突触活动时间连接性，可以利用磁性隧道结电阻态随机翻转实现突触可塑性。另外为了提升突触学习效率，使用两个独特的双层随机突触实现了长时/短时随机突触，证明了报道的这种突触构型以及随机学习算法能够很好的实现MINST手写数字识别的任务。器件的计算效率来源于自旋电子学器件的超低操作能量消耗。

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.