The Key Laboratory of Advanced Microprocessor Chips and Systems, College of Computer, National University of Defense Technology, Changsha 410073, China.
Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China.
ACS Appl Mater Interfaces. 2024 Oct 30;16(43):59088-59095. doi: 10.1021/acsami.4c13405. Epub 2024 Oct 21.
The ventral visual pathway (VVP) of the human brain efficiently implements target recognition by employing a deep hierarchical structure to build complex visual concepts from simple features. Artificial neural networks (ANNs) based on spintronic devices are capable of target recognition, but their poor interpretability and limited network depth hinder ANNs from mimicking the VVP. Hardware implementation of the VVP requires a biorealistic spintronic device as well as the corresponding interpretable and deep network structure, which have not been reported so far. Here, we report a ferrimagnetic neuron with a continuously differentiable exponential linear unit (CeLu) activation function, which is closer to biological neurons and could mitigate the issue of limited network depth. Meanwhile, we also demonstrate that a ferrimagnet can construct artificial synapses with high linearity and symmetry to meet the requirements of weight update algorithms. Based on these neurons and synapses, we propose an all-spin convolutional neural network (CNN) with a high interpretability and deep neural network, to mimic the VVP. Compared to the state-of-the-art spintronic-based neuromorphic computing model, the CNN with bionic function, using experimentally derived device parameters, achieves high recognition accuracies of over 91% and 98% on the CIFAR-10 datasets and the MNIST datasets, respectively, showing improvements of 1.13% and 1.76%. Our work provides a promising method to improve the bionic performance of spintronic device-based neural networks.
人类大脑的腹侧视觉通路 (VVP) 通过采用深层层次结构，从简单特征构建复杂的视觉概念，从而有效地实现目标识别。基于自旋电子器件的人工神经网络 (ANN) 能够实现目标识别，但由于其较差的可解释性和有限的网络深度，限制了它们模仿 VVP 的能力。VVP 的硬件实现需要一个具有生物逼真特性的自旋电子器件，以及相应的可解释性和深度网络结构，到目前为止还没有报道。在这里，我们报告了一种具有连续可微指数线性单元 (CeLu) 激活函数的亚铁磁神经元，它更接近生物神经元，可以缓解网络深度有限的问题。同时，我们还证明了亚铁磁体可以构建具有高线性度和对称性的人工突触，以满足权重更新算法的要求。基于这些神经元和突触，我们提出了一种具有高可解释性和深度神经网络的全自旋卷积神经网络 (CNN)，以模拟 VVP。与最先进的基于自旋电子的神经形态计算模型相比，使用实验得出的器件参数的仿生功能 CNN 在 CIFAR-10 数据集和 MNIST 数据集上的识别准确率分别超过 91%和 98%，分别提高了 1.13%和 1.76%。我们的工作为提高基于自旋电子器件的神经网络的仿生性能提供了一种很有前途的方法。
