用四个合旋转矩纳米振荡器识别元音
Miguel Romera1, Philippe Talatchian1, Sumito Tsunegi2
1Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.
Nature
|October 31, 2018
概括
这项研究展示了使用自旋式纳米振荡器的人工智能 (AI) 硬件的新方法. 这些动态系统可以通过同步学习和识别复杂的语音模式.
科学领域:
- 神经科学与人工智能
- 螺旋电子和纳米电子
背景情况:
- 人工神经网络 (ANN) 通常使用静态函数和标准算法.
- 神经启发的计算探索动态系统,模仿大脑的功能,
- 新兴的纳米电子设备为硬件实现提供紧,节能的非线性自动振荡器.
研究的目的:
- 解决动态纳米电子神经网络中的学习挑战.
- 证明使用自旋纳米振荡器实现硬件学习的调整性.
- 展示小型动态神经网络的模式分类能力.
主要方法:
- 使用四个旋转扭矩纳米振荡器的硬件网络.
- 使用自动实时学习规则调整振荡器频率.
- 利用合振荡器的同步现象进行计算.
主要成果:
- 成功训练了纳米振荡器网络识别口语元音.
- 通过振荡器同步实现了高的实验识别率.
- 证明非线性动态特征能够在小型硬件网络中进行非微不足道的模式分类.
结论:
- 螺旋式纳米振荡器的广泛调整性是实现动态神经形态硬件学习的关键.
- 在合的纳米振荡器中同步方便复杂的计算任务.
- 这种方法为节能,紧的AI硬件提供了途径.
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