This work proposes a novel three-terminal magnetic tunnel junction (MTJ) as a stochastic neuron. The neuron is probabilistically switched based on the voltage-controlled magnetic anisotropy (VCMA) effect with the assistance of Rashba effective field. We find that a restricted Boltzmann machine (RBM) implemented using our proposed neuron for handwritten character recognition can achieve synaptic weight sparsity, without sacrificing the network classification accuracy. Moreover, the RBM implemented by this novel neuron performs even better in the presence of device variations, implying that our device is highly suitable for the hardware implementation of RBM.

中文翻译：

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具有重量稀疏性的受限玻尔兹曼机的压控自旋电子随机神经元

这项工作提出了一种新颖的三端磁隧道结（MTJ）作为随机神经元。神经元基于压控磁各向异性 (VCMA) 效应在 Rashba 有效场的帮助下进行概率切换。我们发现使用我们提出的用于手写字符识别的神经元实现的受限玻尔兹曼机 (RBM) 可以实现突触权重稀疏，而不会牺牲网络分类的准确性。此外，由这种新型神经元实现的 RBM 在存在设备变化的情况下表现更好，这意味着我们的设备非常适合 RBM 的硬件实现。