A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation.,Faraday Discussions

当前位置： X-MOL 学术 › Faraday Discuss. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation.
Faraday Discussions ( IF 3.3 ) Pub Date : 2019-02-18 , DOI: 10.1039/c8fd00114f
Melika Payvand ₁ , Manu V Nair , Lorenz K Müller , Giacomo Indiveri

Affiliation

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.

中文翻译：

使用非理想忆阻设备进行内存计算的神经形态系统方法：从缓解到利用。

忆阻器件代表了用于构建神经形态电子系统的有前途的技术。除了其紧凑性和非易失性之外，它们还具有与计算相关的物理特性，例如其状态相关性，非线性电导率变化以及其开关阈值和电导率值的固有变化性，这使其成为理想的器件。用于模拟真实突触的生物物理。在本文中，我们提出了一种尖峰神经网络体系结构，该体系结构支持使用忆阻器设备作为突触元件，并提出了混合信号模数接口电路，以减轻电导值变化的影响并利用其开关阈值的变化来实现。随机学习。使用以互补推挽机制配置并与电流模式归一化电路接口的成对忆阻器件对，可以减轻器件可变性的影响。随机学习机制是通过将所需的突触权重变化映射到相应的切换概率而获得的，该切换概率是从忆阻设备的固有随机行为中得出的。我们演示了CMOS电路的功能，并将所提出的体系结构应用于基于MNIST数据集的标准神经网络手写数字分类基准。我们使用行为级别的尖峰神经网络仿真评估该基准测试中提出的方法的性能，

更新日期：2019-02-19

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11