To fully unleash the potential of graphene-based devices for neuromorphic computing, we propose a graphene synapse and a graphene neuron that form together a basic Spiking Neural Network (SNN) unit, which can potentially be utilized to implement complex SNNs. Specifically, the proposed synapse enables two fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity, and both Long-Term Potentiation (LTP) and Long-Term Depression (LTD) can be emulated with the same structure by properly adjusting its bias. The proposed neuron captures the essential Leaky Integrate and Fire spiking neuron behavior with post firing refractory interval. We demonstrate the proper operation of the graphene SNN unit by relying on a mixed simulation approach that embeds the high accuracy of atomistic level simulation of graphene structures conductance within the SPICE framework. Subsequently, we analyze the way graphene synaptic plasticity affects the behavior of a 2-layer SNN example consisting of 6 neurons and demonstrate that LTP significantly increases the number of firing events while LTD is diminishing them, as expected. To assess the plausibility of the graphene SNN reaction to input stimuli we simulate its behavior by means of both SPICE and NEST, a well established SNN simulation framework, and demonstrate that the obtained reactions, characterized in terms of total number of firing events and mean Inter-Spike Interval (ISI) length, are in close agreement, which clearly suggests that the proposed design exhibits a proper behavior. Further, we prove the unsupervised learning capabilities of the proposed design by considering a 2-layer SNN consisting of 30 neurons meant to recognize the characters “A,” “E,” “I,” “O,” and “U,” represented with a 5 by 5 black and white pixel matrix. The SPICE simulation results indicate that the graphene SNN is able to perform unsupervised character recognition associated learning and that its recognition ability is robust to input character variations. Finally, we note that our proposal results in a small real-estate footprint (max. 30 nm $^2$ are required by one graphene-based device) and operates at 200 mV supply voltage, which suggest its suitability for the design of large-scale energy-efficient computing systems.

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具有无监督学习能力的紧凑型基于石墨烯的尖峰神经网络

为了充分释放基于石墨烯的设备用于神经形态计算的潜力，我们提出了一个石墨烯突触和一个石墨烯神经元，它们共同构成一个基本的尖峰神经网络（SNN）单元，可以潜在地用于实现复杂的SNN。具体而言，拟议的突触启用两个基本的突触功能，即尖峰时间依赖性可塑性（STDP）和长期可塑性，并且长期增强（LTP）和长期抑郁（LTD）都可以用通过适当地调整其偏差来调整相同的结构。拟议中的神经元具有基本的泄漏积分和击发后放电难治性间隔的火刺神经元行为。我们依靠混合仿真方法证明了石墨烯SNN单元的正确运行，该方法将高精度的石墨烯结构电导原子级仿真嵌入到SPICE框架中。随后，我们分析了石墨烯突触可塑性如何影响由6个神经元组成的2层SNN示例的行为，并证明了LTP显着增加了触发事件的数量，而LTD则减少了触发事件，这与预期的一样。为了评估石墨烯SNN反应对输入刺激的合理性，我们通过既定的SNN模拟框架SPICE和NEST来模拟其行为，并证明了所获得的反应的特征在于发射事件的总数和均值-峰值间隔（ISI）长度密切相关，这清楚表明所建议的设计表现出适当的行为。此外，我们通过考虑由30个神经元组成的2层SNN来证明所提议设计的无监督学习能力，这些神经元旨在识别表示的字符“ A”，“ E”，“ I”，“ O”和“ U”具有5 x 5的黑白像素矩阵。SPICE仿真结果表明，石墨烯SNN能够执行无监督的字符识别相关学习，并且其识别能力对于输入字符变化具有鲁棒性。最后，我们注意到我们的提案导致了较小的房地产占地面积（最大30 nm ”和“ U”，分别用5 x 5的黑白像素矩阵表示。SPICE仿真结果表明，石墨烯SNN能够执行无监督的字符识别相关学习，并且其识别能力对于输入字符变化具有鲁棒性。最后，我们注意到我们的提案导致了较小的房地产占地面积（最大30 nm ”和“ U”，分别用5 x 5的黑白像素矩阵表示。SPICE仿真结果表明，石墨烯SNN能够执行无监督的字符识别相关学习，并且其识别能力对于输入字符变化具有鲁棒性。最后，我们注意到我们的提案导致了较小的房地产占地面积（最大30 nm $ ^ 2 $ 一个基于石墨烯的设备所需的电压）并在200 mV的电源电压下工作，这表明它适用于大规模节能计算系统的设计。