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首页> 外文会议>IEEE International System-on-Chip Conference >A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems
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A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems

机译：神经晶体系统依赖学习的尖峰定时的双映映像突触

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Neuromorphic systems consist of a framework of spiking neurons interconnected via plastic synaptic junctures. The discovery of a two terminal passive nanoscale memristive device has spurred great interest in the realization of memristive plastic synapses in neural networks. In this work, a synapse structure is presented that utilizes a pair of memristors, to implement both positive and negative weights. The working scheme of this synapse as an electrical interlink between neurons is explained, and the relative timing of their spiking events is analyzed, which leads to a modulation of the synaptic weight in accordance with the spike-timing-dependent plasticity (STDP) rule. A digital pulse width modulation technique is proposed to achieve these variable changes to the synaptic weight. The synapse architecture presented is shown to have high accuracy when used in neural networks for classification tasks. Lastly, the energy requirement of the system during various phases of operation is presented.
机译：神经形态系统包括通过塑料突触时隙互连的尖峰神经元的框架。两个端子被动纳米级忆误器件的发现对神经网络中的椎间塑料突触的实现感到非常兴趣。在这项工作中，提出了一种利用一对存储器的突触结构来实现正极和负重。解释作为神经元之间的电互链接的这种突触的工作方案，分析了它们的尖峰事件的相对定时，这导致根据峰值定时依赖性塑性（STDP）规则的调制。提出了一种数字脉冲宽度调制技术，以实现对突触重量的这些变量变化。展示的Synapse架构显示在用于分类任务的神经网络中使用时具有高精度。最后，提出了在操作各阶段期间系统的能量要求。

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