期刊
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
卷 26, 期 12, 页码 2806-2815出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2018.2818978
关键词
Neuromorphic networks; online learning; pattern learning; resistive switching memory (RRAM); spike-rate dependent plasticity (SRDP)
资金
- European Research Council through the European Union's Horizon 2020 Research and Innovation Program [648635]
D Mimicking the cognitive functions of the brain in hardware is a primary challenge for several fields, including device physics, neuromorphic engineering, and biological neuroscience. A key element in cognitive hardware systems is the ability to learn via biorealistic plasticity rules, combined with the area scaling capability to enable integration of high-density neuron/synapse networks. To this purpose, resistive switching memory (RRAM) devices have recently attracted a strong interest as potential synaptic elements. Here, we present a novel hybrid 4-transistors/1-resistor synapse capable of spike-rate-dependent plasticity. The frequency-dependent learning behavior of the synapse is shown by experiments on HfO2 RRAM devices. Unsupervised learning, update, and recognition of one or more visual patterns in sequence is demonstrated at the level of neural network, thus, supporting the feasibility of hybrid CMOS/RRAM integrated circuits matching the learning capability in the human brain.
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