期刊
IEEE TRANSACTIONS ON NANOTECHNOLOGY
卷 12, 期 3, 页码 288-295出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNANO.2013.2250995
关键词
Memristive devices; memristors; neuromorphic; spike timing dependent plasticity (STPD); spiking neural networks; unsupervised learning
类别
资金
- European Union [FP7-216777]
- Centre National de la Recherche Scientifique/Institute for Engineering and Systems Sciences (PEPS Synapse)
Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized spike timing dependent plasticity rule. In the network, neurons' threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices' behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices' parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices' conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据