Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems
Mahmoodi, Mohammad Reza
Roldan, Juan B.
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/676322
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AbstractMemristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.
CitationZhu, K., Mahmoodi, M. R., Fahimi, Z., Xiao, Y., Wang, T., Bukvišová, K., Kolíbal, M., Roldan, J. B., Perez, D., Aguirre, F., & Lanza, M. (2022). Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems. Advanced Intelligent Systems, 2200001. Portico. https://doi.org/10.1002/aisy.202200001
SponsorsSupported by the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the National Natural Science Foundation of China (grants no. 11661131002, 61874075), and the Ministry of Finance of China (grant no. SX21400213), the 111 Project from the State Administration of Foreign Experts Affairs of China, the Collaborative Innovation Centre of Suzhou Nano Science &Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, and the Priority Academic Program Development of Jiangsu Higher Education Institutions. M.L. acknowledges generous support from the Baseline funding program of the King Abdullah University of Science and Technology. Czech Nano Lab projectLM2018110 funded by MEYS CR is gratefully acknowledged for the financial support of the measurements at CEITEC Nano Research Infrastructure.
JournalAdvanced Intelligent Systems
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