Reservoir computing with the frequency, phase, and amplitude of spin-torque nano-oscillators
Reservoir computing with the frequency, phase, and amplitude of spin-torque nano-oscillators
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DOI：
10.1063/1.5079305
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发表时间：
2019-01-07
影响因子：
4
通讯作者：
Grollier, J.
中科院分区：
文献类型：
--
作者：
Markovic, D.;Leroux, N.;Grollier, J.
自旋扭矩纳米振荡器可以模拟纳米级的神经元。最近的工作表明，可以利用其振荡幅度的非线性来实现对以输入电压幅度编码的输入信号的波形分类。在这里，我们表明振荡器的频率和相位也可以用于识别波形。为此，我们将振荡器锁相到输入波形，该波形在其调制频率中携带信息。通过这种方式，我们大大降低了幅度、相位和频率噪声。我们证明，在对振荡器幅度、相位或频率的输出进行解码时，该方法可以对正弦波和方波进行分类，准确度高于 99%。我们发现识别率与每个变量的噪声和非线性直接相关。这些结果证明，自旋扭矩纳米振荡器提供了一个有趣的平台，可以利用其丰富的动态特性来实现不同的计算方案。由 AIP Publishing 许可发布。
Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can he leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here, we show that the frequency and phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way, we considerably decrease the amplitude, phase, and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase, or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features. Published under license by AIP Publishing.