神经形态工程学
计算机科学
尖峰神经网络
自旋电子学
突触重量
记忆电阻器
能源消耗
高效能源利用
人工神经网络
人工智能
电气工程
物理
工程类
量子力学
铁磁性
作者
Di Wang,Ruifeng Tang,Huai Lin,Long Liu,Nuo Xu,Yan Sun,Xuefeng Zhao,Ziwei Wang,Di Wang,Zhihong Mai,Yongjian Zhou,Nan Gao,Cheng Song,Lijun Zhu,Tom Wu,Ming Liu,Guozhong Xing
标识
DOI:10.1038/s41467-023-36728-1
摘要
Abstract Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore’s law. However, an ideal artificial neuron possessing bio-inspired behaviors as exemplified by the requisite leaky-integrate-fire and self-reset (LIFT) functionalities within a single device is still lacking. Here, we report a new type of spiking neuron with LIFT characteristics by manipulating the magnetic domain wall motion in a synthetic antiferromagnetic (SAF) heterostructure. We validate the mechanism of Joule heating modulated competition between the Ruderman–Kittel–Kasuya–Yosida interaction and the built-in field in the SAF device, enabling it with a firing rate up to 17 MHz and energy consumption of 486 fJ/spike. A spiking neuron circuit is implemented with a latency of 170 ps and power consumption of 90.99 μW. Moreover, the winner-takes-all is executed with a current ratio >10 4 between activated and inhibited neurons. We further establish a two-layer spiking neural network based on the developed spintronic LIFT neurons. The architecture achieves 88.5% accuracy on the handwritten digit database benchmark. Our studies corroborate the circuit compatibility of the spintronic neurons and their great potential in the field of intelligent devices and neuromorphic computing.
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