Li Xinyi, Zhong Yanan, Chen Hang, Tang Jianshi, Zheng Xiaojian, Sun Wen, Li Yang, Wu Dong, Gao Bin, Hu Xiaolin, Qian He, Wu Huaqiang
School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China.
Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China.
Adv Mater. 2023 Sep;35(37):e2203684. doi: 10.1002/adma.202203684. Epub 2022 Jul 11.
Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy-efficient neuromorphic computing. To this end, a titanium oxide (TiO )-based interface-type dynamic memristor and an niobium oxide (NbO )-based Mott memristor are integrated as an artificial dendrite and spike-firing soma, respectively, to construct a dendritic neuron unit for realizing high-efficiency spatial-temporal information processing. Further, a dendritic neural network is hardware-implemented for spatial-temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University-Red Green Blue (NTU-RGB) dataset as a benchmark spatial-temporal task; it shows a nearly 20% improvement in accuracy for the memristors-based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio-plausible neural networks that can manage complex spatial-temporal tasks with high efficiency.
多样的微观离子动力学有助于介导生物神经网络以低能耗处理复杂任务的能力。因此，基于过渡金属氧化物的忆阻器中丰富的内部离子动力学有望为实现节能神经形态计算提供一个独特且有用的平台。为此，分别将基于氧化钛（TiO）的界面型动态忆阻器和基于氧化铌（NbO）的莫特忆阻器集成作为人工树突和发放尖峰的胞体，以构建用于实现高效时空信息处理的树突神经元单元。此外，为了突出通过在网络中纳入树突功能所实现的计算优势，对用于时空信息处理的树突神经网络进行了硬件实现。以南洋理工大学红-绿-蓝（NTU-RGB）数据集作为基准时空任务进行了人体运动识别演示；结果表明，与图形处理单元（GPU）相比，包含树突的基于忆阻器的硬件在准确率上提高了近20%，在功率效率上具有1000倍的优势。本研究中开发的树突神经元可被视为实现更具生物合理性的神经网络的关键构建模块，该神经网络能够高效管理复杂的时空任务。
