Truong Son Ngoc, Ham Seok-Jin, Min Kyeong-Sik
School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.
Nanoscale Res Lett. 2014 Nov 23;9(1):629. doi: 10.1186/1556-276X-9-629. eCollection 2014.
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.
本文提出了一种用于语音识别的基于二元忆阻器的神经形态交叉开关电路。基于丝状开关机制的二元忆阻器比模拟忆阻器更常见且易于制造，模拟忆阻器材料稀缺且制造工艺更为复杂。因此，我们开发了一种使用丝状开关二元忆阻器而非界面开关模拟忆阻器的神经形态交叉开关电路。所提出的二元忆阻器交叉开关能够通过4位64输入通道识别五个元音。所提出的交叉开关通过2500个语音样本进行测试，并验证能够识别89.2%的测试样本。从统计模拟结果来看，尽管忆阻值的百分比变化从0%大幅增加到15%，二元忆阻器交叉开关的识别率仅从89.2%略微下降至80%。相比之下，对于相同的忆阻值百分比变化，模拟忆阻器交叉开关的识别率则从96%大幅降至9%。
