Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

中文翻译：

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硬件无监督学习的鲁棒忆阻脉冲神经形态系统设计

尖峰神经网络 (SNN) 通过将信息编码为尖峰，提供了一种节能、生物学上合理的学习范式。忆阻器的发现加速了尖峰神经形态系统的发展，因为该设备的内在可塑性使其成为模拟生物突触的理想候选者。尽管提供了纳米级外形尺寸、非易失性和低功耗运行，但忆阻器仍存在设备级非理想特性，这会影响系统级性能。为了解决这些问题，本文提出了一种基于忆阻交叉杆的神经形态系统，该系统使用具有双忆阻器突触、全数字脉冲宽度调制的尖峰定时依赖可塑性和稳态神经元的无监督学习。实现的单层 SNN 被应用于对手写数字进行分类的模式识别任务。通过改变设计参数（例如训练时期数、神经元和电容器）来分析系统的性能。此外，还研究了忆阻器器件非理想性的影响，例如器件开关失配、老化、故障和工艺变化，并证明了所提出系统的弹性。