Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity
Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity
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DOI：
10.1109/tnano.2018.2821131
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发表时间：
2018-03
影响因子：
2.4
通讯作者：
Nan Zheng;P. Mazumder
中科院分区：
文献类型：
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作者：
Nan Zheng;P. Mazumder
在本文中，我们提出了一种基于回忆录横梁结构的学习系统的方法。学习是在适合硬件的尖峰依赖性可塑性学习规则的帮助下进行的。为了将学习算法应用于基于Memristor的神经网络，进行了几种简化和适应。使用拟议的技术，可以规避进行分裂和代表签名权重的困难。此外，当应用于实用的Memristor设备时，考虑了不同改变电导的行为来证明所提出的算法和架构的有效性。此外，考虑了神经网络中存在的各种非理想性，包括CMOS神经元和Memristor突触的变化以及与更新突触权重相关的噪音。我们证明了所提出的学习算法和硬件体系结构与大多数变化和噪音相对于大多数。这种学习的鲁棒性是有希望的，因为Memristor设备的变化和随机行为通常很重要。 MNIST手写数字识别任务基于提议的学习算法的基于Memristor的神经网络是基准的。证明了高达97.10％的识别精度。
In this paper, we propose a methodology to design learning systems based on a memristor crossbar structure. Learning is carried out with the help of a hardware-friendly spike-timing-dependent plasticity learning rule. Several simplifications and adaptations are made in order to apply the learning algorithm to memristor-based neural networks. The difficulties in conducting division and representing signed weights are circumvented using the proposed techniques. In addition, different conductance-changing behaviors are considered to demonstrate the effectiveness of the proposed algorithm and architecture when applied to practical memristor devices. Furthermore, various nonidealities existing in the neural network, including variations in CMOS neurons and memristor synapses and noises associated with updating the synaptic weights, are considered. We demonstrate that the proposed learning algorithm and hardware architecture are robust against most variations and noises. This robustness of learning is promising, as variations and stochastic behaviors of memristor devices are usually substantial. Memristor-based neural networks with the proposed learning algorithm are benchmarked with the MNIST handwritten digits recognition task. A recognition accuracy as high as 97.10% is demonstrated.