通过突发模式通信和实时无监督机器学习实现低功耗STDP的新型rram启用1T1R突触

2016 IEEE Symposium on VLSI Technology Pub Date : 2016-06-14 DOI:10.1109/VLSIT.2016.7573432

S. Ambrogio, S. Balatti, V. Milo, R. Carboni, Z. Wang, A. Calderoni, N. Ramaswamy, D. Ielmini

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引用次数: 32

摘要

我们提出了一种新的用于神经形态计算的电子突触，它由基于HfO2 RRAM技术的1T1R结构组成，具有STDP和模式学习能力。采用短脉冲尖峰和突发模式集成，降低了功耗。MNIST分类显示出良好的学习效率和分类效率。这些结果支持RRAM作为低功耗神经形态硬件的使能技术。

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Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning

We present a new electronic synapse for neuromorphic computing consisting of a 1T1R structure based on HfO2 RRAM technology, and capable of STDP and pattern learning. Power consumption is reduced by adopting short POST spike and burst-mode integration. MNIST classification shows promising learning and classification efficiency. These results support RRAM as an enabling technology for low-power neuromorphic hardware.

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2016 IEEE Symposium on VLSI Technology

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