Guo Zhenqiang, Duan Guojun, Zhang Yinxing, Sun Yong, Zhang Weifeng, Li Xiaohan, Shi Haowan, Li Pengfei, Zhao Zhen, Xu Jikang, Yang Biao, Faraj Yousef, Yan Xiaobing
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
College of Electron and Information Engineering, Hebei University, Baoding, 071002, China.
Adv Sci (Weinh). 2025 Feb;12(8):e2411925. doi: 10.1002/advs.202411925. Epub 2025 Jan 4.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption. Here, robust and epitaxial Gd: HfO2-based film memristors are reported and construct a weighted echo state graph neural network (WESGNN). Benefiting from the optimized epitaxial films, the high switching speed (20 ns), low energy consumption (2.07 fJ), multi-value storage (4 bits), and high endurance (10) outperform most memristors. Notably, thanks to the appropriately dispersed conductance distribution (standard deviation = 7.68 nS), the WESGNN finely regulates the relative weights of input nodes and recursive matrix to realize state-of-the-art performance using the MUTAG and COLLAB datasets for graph classification tasks. Overall, robust and epitaxial film memristors offer nanoscale scalability, high reliability, and low energy consumption, making them energy-efficient hardware solutions for graph learning applications.
针对图神经网络学习需求定制的硬件系统将提高图结构数据的处理效率和强大的时间处理能力。然而，由于导电细丝的随机生长，大多数基于非晶/多晶氧化物的忆阻器通常具有不稳定的电导调节。而基于坚固且外延膜忆阻器的图神经网络因其高耐久性和超低功耗，尤其能够提高能源效率。在此，报道了坚固且外延的基于Gd:HfO2的膜忆阻器，并构建了加权回声状态图神经网络（WESGNN）。受益于优化的外延膜，其高开关速度（20纳秒）、低能耗（2.07飞焦）、多值存储（4位）和高耐久性（10次）优于大多数忆阻器。值得注意的是，由于电导分布适当分散（标准差 = 7.68纳秒），WESGNN通过使用用于图分类任务的MUTAG和COLLAB数据集，精细地调节输入节点和递归矩阵的相对权重，以实现最先进的性能。总体而言，坚固且外延的膜忆阻器具有纳米级可扩展性、高可靠性和低能耗，使其成为图学习应用中节能的硬件解决方案。
