In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity functionalities and a spiking neuronal array, onto which various neural network structures can be mapped according to the application requirements and constraints. To demonstrate the validity of the synaptic weights training methodology and the suitability of the proposed SNN architecture for practical utilization, we consider character recognition and edge detection applications. In each case, the graphene-based platform is configured as per the application tailored SNN topology and initial state and SPICE simulated to evaluate its reaction to the applied input stimuli. For the first application, a 2-layer SNN is used to perform character recognition for 5 vowels. Our simulation indicates that the graphene-based SNN can achieve comparable recognition accuracy with the one delivered by a functionally equivalent Artificial Neural Network. Further, we reconfigure the architecture for a 3-layer SNN to perform edge detection on 2 grayscale images. SPICE simulation results indicate that the edge extraction results are close agreement with the one produced by classical edge detection operators. Our results suggest the feasibility and flexibility of the proposed approach for various application purposes. Moreover, the utilized graphene-based synapses and neurons operate at low supply voltage, consume low energy per spike, and exhibit small footprints, which are desired properties for largescale energy-efficient implementations.

中文翻译：

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一种可重构的基于石墨烯的尖峰神经网络架构


在本文中，我们提出了一种基于可重构石墨烯的尖峰神经网络（SNN）架构和一种用于确定初始突触权重值的训练方法。所提出的基于石墨烯的平台是灵活的，包括可配置为不同的初始突触权重和可塑性功能的可编程突触阵列和尖峰神经元阵列，可根据应用要求和约束将各种神经网络结构映射到其上。为了证明突触权重训练方法的有效性以及所提出的 SNN 架构在实际应用中的适用性，我们考虑了字符识别和边缘检测应用。在每种情况下，基于石墨烯的平台都根据应用定制的 SNN 拓扑和初始状态进行配置，并模拟 SPICE 以评估其对所应用的输入刺激的反应。对于第一个应用，使用 2 层 SNN 来执行 5 个元音的字符识别。我们的模拟表明，基于石墨烯的 SNN 可以实现与功能等效的人工神经网络所提供的识别精度相当的识别精度。此外，我们重新配置了 3 层 SNN 的架构，以对 2 个灰度图像执行边缘检测。 SPICE仿真结果表明，边缘提取结果与经典边缘检测算子产生的结果非常一致。我们的结果表明所提出的方法对于各种应用目的的可行性和灵活性。此外，所使用的基于石墨烯的突触和神经元在低电源电压下运行，每个尖峰消耗的能量低，并且占地面积小，这是大规模节能实施所需的特性。