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Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition

机译:具有纳米级丝状开关二进制忆阻器的神经形态交叉开关电路用于语音识别

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摘要

In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. Thus, we develop a neuromorphic crossbar circuit using filamentary-switching binary memristors not using interface-switching analog memristors. The proposed binary memristor crossbar can recognize five vowels with 4-bit 64 input channels. The proposed crossbar is tested by 2,500 speech samples and verified to be able to recognize 89.2% of the tested samples. From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%. In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.
机译:本文提出了一种具有二进制忆阻器的神经形态纵横制电路,用于语音识别。与基于材料的稀有且需要更复杂的制造工艺的模拟忆阻器相比,基于丝状开关机制的二元忆阻器更为流行,并且易于制造。因此,我们开发了使用丝状开关二进制忆阻器而不使用接口切换模拟忆阻器的神经形态交叉开关电路。拟议的二进制忆阻器纵横开关可以识别具有4位64输入通道的五个元音。拟议的交叉开关由2,500个语音样本进行测试,并经过验证能够识别89.2%的测试样本。从统计模拟来看,尽管忆阻的百分比变化从0%增加到15%,但二进制忆阻器交叉开关的识别率估计几乎不会从89.2%降低到80%。相反,对于相同的忆阻百分比变化,模拟忆阻器纵横开关的识别率会从96%显着降低到9%。

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