Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches
Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches
复制标题
具有纳米级忆阻设备的仿生网络，结合了无监督和监督学习方法
DOI：
10.1145/2765491.2765528
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
C. Gamrat
中科院分区：
文献类型：
--
作者：
D. Querlioz;Weisheng Zhao;P. Dollfus;Jacques;O. Bichler;C. Gamrat
这项工作提出了两种基于忆阻纳米器件的学习架构。首先，我们提出了一个无监督的架构，能够辨别未标记的输入中的特征。忆阻纳米器件被用作突触，并通过简单的电压脉冲进行学习，该电压脉冲实现了简化的“尖峰定时依赖可塑性”规则。通过系统仿真，证明了该方案的有效性，在识别率方面的教科书的情况下，字符识别。仿真也显示了它对器件变化的极端鲁棒性。其次，我们提出了一个监督架构，如果每个输入的分类都给定，它可以学习。仿真结果证明了该方法的有效性。可以看到对设备变化的良好鲁棒性，但没有达到无监督方法的水平。最后，我们表明，这两种方法可以结合起来，变化的鲁棒性高于监督的情况下。这开启了重要的前景，比如首先使用未标记的数据以无监督的方式训练系统的可能性，同时仍然受益于编程监督系统的简单性。
This work proposes two learning architectures based on memristive nanodevices. First, we present an unsupervised architecture that is capable of discerning characteristic features in unlabeled inputs. The memristive nanodevices are used as synapses and learn thanks to simple voltage pulses which implement a simplified “Spike Timing Dependent Plasticity” rule. With system simulation, the efficiency of this scheme is evidenced in terms of recognition rate on the textbook case of character recognition. Simulations also show its extreme robustness to device variations. Second, we present a supervised architecture that can learn if the classification of every input is given. Simulations prove its efficiency. A good robustness to device variation is seen, but not to the level of the unsupervised approach. Finally, we show that both approaches can be combined, with variation robustness higher than in the supervised case. This opens important prospects, like the possibility to first train the system in an unsupervised way with unlabeled data, while still benefiting of the simplicity to program a supervised system.