| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Voltage-Driven Adaptive Spintronic Neuron for Energy-Efficient Neuromorphic Computing |
| Ya-Bo Chen1, Xiao-Kuo Yang1*, Tao Yan2, Bo Wei1, Huan-Qing Cui1, Cheng Li3, Jia-Hao Liu3, Ming-Xu Song1, and Li Cai1 |
1Department of Basic Sciences, Air Force Engineering University, Xi'an 710051, China
2School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, China
3College of Computer, National University of Defense Technology, Changsha 410005, China
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| Cite this article: |
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Ya-Bo Chen, Xiao-Kuo Yang, Tao Yan et al 2020 Chin. Phys. Lett. 37 078501 |
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Abstract A spintronics neuron device based on voltage-induced strain is proposed. The stochastic switching behavior, which can mimic the firing behavior of neurons, is obtained by using two voltage signals to control the in-plane magnetization of a free layer of magneto-tunneling junction. One voltage signal is used as the input, and the other voltage signal can be used to tune the activation function (Sigmoid-like) of spin neurons. Therefore, this voltage-driven tunable spin neuron does not necessarily use energy-inefficient Oersted fields and spin-polarized current. Moreover, a voltage-control reading operation is presented, which can achieve the transition of activation function from Sigmoid-like to ReLU-like. A three-layer artificial neural network based on the voltage-driven spin neurons is constructed to recognize the handwritten digits from the MNIST dataset. For the MNIST handwritten dataset, the design achieves 97.75% recognition accuracy. The present results indicate that the voltage-driven adaptive spintronic neuron has the potential to realize energy-efficient well-adapted neuromorphic computing.
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Received: 17 April 2020
Published: 21 June 2020
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| PACS: |
85.75.-d
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(Magnetoelectronics; spintronics: devices exploiting spin polarized transport or integrated magnetic fields)
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85.80.Jm
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(Magnetoelectric devices)
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87.18.Sn
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(Neural networks and synaptic communication)
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| Fund: Supported by the National Natural Science Foundation of China under Grants Nos. 61804184 and 11975311, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No. 2020JQ470, and the Foundation of Independent Scientific Research under Grant Nos. YNJC19070501, YNJC19070502, and YNJC19070504. |
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