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Evaluation of memory capacity of spin torque oscillator for recurrent neural networks

机译:递归神经网络自旋转矩振荡器的存储容量评估

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We have succeeded in experimentally observing the memory functionality of a vortex-type spin torque oscillator used in recurrent neural networks for the first time. Voltage pulses representing random binary pattern of 2000 (1000) bits were applied to the oscillator for learning (test), and the amplitude of the oscillator was evaluated as the 200 virtual nodes. The memory capacity of the whole system including the oscillator was evaluated to be 1.8 at maximum. The value is larger than the memory capacity of the system without the oscillator, indicating the existence of a finite contribution from the spin torque oscillator. (C) 2018 The Japan Society of Applied Physics
机译:我们已经成功地通过实验观察了首次用于递归神经网络的涡旋型自旋扭矩振荡器的存储功能。将代表2000(1000)位随机二进制模式的电压脉冲施加到振荡器上进行学习(测试),并将振荡器的幅度评估为200个虚拟节点。包括振荡器在内的整个系统的存储容量最大估计为1.8。该值大于没有振荡器的系统的存储容量,表明存在自旋转矩振荡器的有限贡献。 (C)2018日本应用物理学会

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