基于自旋力矩振子阵列的复值Hopfield网络联想存储器
Associative Memories Using Complex-Valued Hopfield Networks Based on Spin-Torque Oscillator Arrays
摘要：
基于自旋力矩振荡器的复值Hopfield网络的仿真可以恢复相位编码图像。忆阻器增强的反相器的序列提供可调谐延迟元件，其通过对振荡器的振荡输出进行相移来实现复权重。伪逆训练足以在一组192个振荡器中存储至少12个图像，表示16个$\times$ 12像素图像。恢复图像所需的能量取决于所需的误差水平。对于这里考虑的振荡器和电路，与理想图像的5%均方根偏差需要大约5 $μ$ s，消耗大约130 nJ。仿真表明，当振荡器的谐振频率可以被调谐为具有小于$10^{-3}$的分数扩展时，网络运行良好，这取决于反馈的强度。
英文摘要：
Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16$\times$12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5 % root mean square deviations from the ideal image require approximately 5 $μ$s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than $10^{-3}$, depending on the strength of the feedback.
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