Subthreshold Spintronic Stochastic Spiking Neural Networks With Probabilistic Hebbian Plasticity and Homeostasis
Subthreshold Spintronic Stochastic Spiking Neural Networks With Probabilistic Hebbian Plasticity and Homeostasis
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DOI：
10.1109/jxcdc.2019.2911046
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发表时间：
2019-04
影响因子：
2.4
通讯作者：
Steven D. Pyle;Ramtin Zand;Shadi Sheikhfaal;R. Demara
中科院分区：
文献类型：
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作者：
Steven D. Pyle;Ramtin Zand;Shadi Sheikhfaal;R. Demara
本文提出的神经采样核心（NSC）提供了一种基于自旋电子器件的电路和学习机制，其利用类似于生物大脑的不精确和随机组件来实现亚阈值电压下的超低功率神经形态计算。利用神经采样的原理，计算神经科学的生物学上合理的理论，提出了一种具有数字突触后电位的自旋电子随机尖峰神经元，结合低精度自旋电子突触，利用新的事件驱动的概率赫布可塑性规则，以及一种新的稳态机制，平衡跨多个时间尺度和过程变化效应的神经活动。主要的计算操作，其相应的突触功效和神经元的稳态参数加权的突触前电位的总和，在一个并行的模拟方式使用嘈杂和不精确的亚阈值组件进行。本文证明，NSC能够学习方向选择性，很像在视觉皮层中发现的简单细胞，以无监督的方式，在每个神经元311 nW和每个活动突触1.9-7.7 nW，使用200 mV的电源电压。
The neural sampling core (NSC) proposed herein offers a spintronic device-based circuit and learning mechanism utilizing imprecise and stochastic components, similar to biological brains, to realize ultralow-power neuromorphic computations at subthreshold voltages. Leveraging principles from neural sampling, a biologically plausible theory from computational neuroscience, a spintronic stochastic spiking neuron with digital Postsynaptic potentials is proposed in conjunction with low-precision spintronic synapses utilizing a new event-driven Probabilistic Hebbian Plasticity Rule, and a novel homeostasis mechanism that balances neural activity across multiple timescales and process variation effects. The primary computational operation, the summation of presynaptic potentials weighted by their corresponding synaptic efficacy and the neuron’s homeostatic parameters, is performed in a parallel analog fashion using noisy and imprecise subthreshold components. It is demonstrated herein that the NSC is capable of learning orientation selectivity, much like the simple cells found in the visual cortex, in an unsupervised fashion at 311 nW per neuron and 1.9–7.7 nW per active synapse using a 200-mV supply voltage.