期刊
ADVANCED MATERIALS
卷 35, 期 37, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202203684
关键词
biological neural networks; dendritic neuron units; ionic dynamics; neuromorphic computing
This study utilizes transition metal oxide-based memristors as artificial dendrites and spike-firing soma to construct dendritic neuron units, achieving high-efficiency spatial-temporal information processing. A hardware-implemented dendritic neural network improves accuracy for human motion recognition and exhibits a 1000x advantage in power efficiency compared to a graphics processing unit.
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 (TiOx)-based interface-type dynamic memristor and an niobium oxide (NbOx)-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 1000x 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.
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