混合压电-磁神经元:节能机器学习的建议

William Scott, Jonathan Jeffrey, Blake Heard, D. Nikonov, I. Young, S. Manipatruni, A. Naeemi, R. M. Iraei
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引用次数: 3

摘要

提出了一种由磁体异质结构和带磁隧道结的压电体(MTJ)组成的自旋电子神经元结构。利用SPICE模型对装置的运行进行了仿真。仿真结果表明,该自旋电子神经元的能量耗散比其他自旋电子神经元的能量耗散提高了70%。与CMOS神经元相比,所提出的神经元占用更小的占地面积,使用更少的能量。由于其通用性和低能量运算,该神经元是人工神经网络(ANN)系统中很有希望采用的候选神经元。
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Hybrid piezoelectric-magnetic neurons: a proposal for energy-efficient machine learning
This paper proposes a spintronic neuron structure composed of a heterostructure of magnets and a piezoelectric with a magnetic tunnel junction (MTJ). The operation of the device is simulated using SPICE models. Simulation results illustrate that the energy dissipation of the proposed neuron compared to that of other spintronic neurons exhibits 70% improvement. Compared to CMOS neurons, the proposed neuron occupies a smaller footprint area and operates using less energy. Owing to its versatility and low-energy operation, the proposed neuron is a promising candidate to be adopted in artificial neural network (ANN) systems.
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