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arXiv 1808.08412physics.app-ph
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具有机器学习兼容架构的基于纳米振荡器的分类

Nano-oscillator-based classification with a machine learning-compatible architecture

Damir Vodenicarevic, Nicolas Locatelli, Julie Grollier, Damien Querlioz

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摘要：

利用耦合纳米振荡器的物理特性的模式分类架构已被证明是有前途的替代计算方法，但缺乏有效的学习算法。在这项工作中，我们提出了一个基于纳米振荡器的分类架构，其中的振荡器的固有频率学习的线性组合的输入，并定义了一个离线学习算法的基础上梯度反向传播。我们的研究结果表明，与在线学习的相关方法相比，分类有了显着的改进。我们还比较了我们的架构与一个简单机械学习情况下，这表明我们的方法是经济的可调参数的数量方面的标准神经网络。所引入的架构也与现有的纳米技术兼容：该架构不需要改变纳米振荡器之间的耦合，并且可以容忍振荡器相位噪声。

英文摘要：

Pattern classification architectures leveraging the physics of coupled nano-oscillators have been demonstrated as promising alternative computing approaches, but lack effective learning algorithms. In this work, we propose a nano-oscillator based classification architecture where the natural frequencies of the oscillators are learned linear combinations of the inputs, and define an offline learning algorithm based on gradient back-propagation. Our results show significant classification improvements over a related approach with online learning. We also compare our architecture with a standard neural network on a simple machine learning case, which suggests that our approach is economical in terms of numbers of adjustable parameters. The introduced architecture is also compatible with existing nano-technologies: the architecture does not require changes in the coupling between nano-oscillators, and it is tolerant to oscillator phase noise.

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