Institute of Neuroinformatics, University of Zurich, ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
Faraday Discuss. 2019 Feb 18;213(0):487-510. doi: 10.1039/c8fd00114f.
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility, they are characterized by their computationally relevant physical properties, such as their state-dependence, non-linear conductance changes, and intrinsic variability in both their switching threshold and conductance values, that make them ideal devices for emulating the bio-physics of real synapses. In this paper we present a spiking neural network architecture that supports the use of memristive devices as synaptic elements and propose mixed-signal analog-digital interfacing circuits that mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold for implementing stochastic learning. The effect of device variability is mitigated using pairs of memristive devices configured in a complementary push-pull mechanism and interfaced to a current-mode normalizer circuit. The stochastic learning mechanism is obtained by mapping the desired change in synaptic weight into a corresponding switching probability that is derived from the intrinsic stochastic behavior of memristive devices. We demonstrate the features of the CMOS circuits and apply the architecture proposed to a standard neural network hand-written digit classification benchmark based on the MNIST data-set. We evaluate the performance of the approach proposed in this benchmark using behavioral-level spiking neural network simulation, showing both the effect of the reduction in conductance variability produced by the current-mode normalizer circuit and the increase in performance as a function of the number of memristive devices used in each synapse.
忆阻器器件是构建神经形态电子系统的一种很有前途的技术。除了其紧凑性和非易失性外，它们还具有与计算相关的物理特性，例如状态依赖性、非线性电导变化，以及其开关阈值和电导值的固有可变性，这使得它们成为模拟真实突触生物物理学的理想器件。在本文中，我们提出了一种尖峰神经网络架构，支持使用忆阻器作为突触元件，并提出了混合信号模拟-数字接口电路，以减轻其电导值变化的影响，并利用其开关阈值的可变性来实现随机学习。通过使用配置在互补推挽机制中的一对忆阻器并将其连接到电流模式归一化器电路，来减轻器件可变性的影响。通过将所需的突触权重变化映射到源自忆阻器固有随机行为的相应开关概率，来获得随机学习机制。我们展示了 CMOS 电路的特点，并将所提出的架构应用于基于 MNIST 数据集的标准神经网络手写数字分类基准。我们使用行为级尖峰神经网络仿真来评估该基准中提出的方法的性能，展示了电流模式归一化器电路产生的电导变化可变性的降低效果，以及随着每个突触中使用的忆阻器数量的增加而提高的性能。
