基于局部抑制后神经元的随机存储器模式识别系统

2017 Silicon Nanoelectronics Workshop (SNW) Pub Date : 2017-06-01 DOI:10.23919/SNW.2017.8242340

Zheng Zhou, Peng Huang, Yuning Jiang, Zhe Chen, Chen Liu, Lifeng Liu, Xiaoyan Liu, Jinfeng Kang

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引用次数: 0

摘要

提出了一种基于随机存储器的后神经元局部抑制模式识别系统。该系统能够学习整个MNIST训练集(60,000个模式)。通过使用该系统，在相同的训练课程中使用相似的模式激发相同的后神经元，从而降低了硬件成本。通过局部抑制后神经元，系统的识别准确率达到90.73%以上。

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RRAM-based pattern recognition system with locally inhibited post-neurons

A novel RRAM-based pattern recognition system with locally inhibited post-neurons is developed. The system is able to learn the whole MNIST training set (60,000 patterns). By using the system, the same post-neuron is fired by the similar patterns in the same training class, which causes the reduction of hardware cost. With the locally inhibited post-neuron, the system can achieve more than 90.73% recognition accuracy.

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2017 Silicon Nanoelectronics Workshop (SNW)

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