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Ultrafast and Energy-Efficient Ferrimagnetic XNOR Logic Gates for Binary Neural Networks
IEEE Electron Device Letters ( IF 4.1 ) Pub Date : 2021-02-26 , DOI: 10.1109/led.2021.3062382
Guanda Wang , Yue Zhang , Zhizhong Zhang , Zhenyi Zheng , Kun Zhang , Jinkai Wang , Jacques-Olivier Klein , Dafine Ravelosona , Weisheng Zhao

Ultrafast current-driven domain wall (DW) motions have been realized in ferrimagnetic (FiM) nanowires. However, the FiM dynamics can be significantly affected by the Joule-heating. In this work, we propose a highly efficient XNOR logic gate by properly leveraging the thermal effect on the FiM DW motions. Its functionality and advantageous performance have been confirmed by the micromagnetic simulations. Moreover, majority logic and full adder functions can also be reconfigured based on the proposed scheme. Lastly, a fully FiM DW based binary neural network (BNN) is built, which provides low energy consumption, short delay and excellent accuracy.

中文翻译:

用于二进制神经网络的超快速节能的铁磁性XNOR逻辑门

在铁磁(FiM)纳米线中已经实现了超快电流驱动的畴壁(DW)运动。但是,焦耳加热会极大地影响FiM动力学。在这项工作中,我们通过适当利用热量对FiM DW运动的影响,提出了一种高效的XNOR逻辑门。它的功能性和优越的性能已经通过微磁仿真得到了证实。而且,多数逻辑和完整的加法器功能也可以根据所提出的方案进行重新配置。最后,构建了完全基于FiM DW的二进制神经网络(BNN),它具有低能耗,短延迟和卓越的准确性。
更新日期:2021-03-26
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