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

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
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.

No MeSH data available.


Voltage waveforms of the binary memristor crossbar and winner-take-all circuits.
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Figure 7: Voltage waveforms of the binary memristor crossbar and winner-take-all circuits.

Mentions: The simulated waveforms of VCa, VCi, VCu, VCe, and VCo are shown in Figure 6. Here, VCa seems to be discharged by GND faster than the other capacitor nodes of VCi, VCu, VCe, and VCo. It means that the voice input matches with the vowel ‘a’ better than the other vowels. The timing diagram of important signals in Figure 4a,b is shown in Figure 7. When the CLK signal is low, all the capacitor nodes of VCa, VCi, VCu, VCe, and VCo are precharged by VDD. At this time, VCa, VCi, VCu, VCe, and VCo are higher than VREF; thus, Da, Di, Du, De, and Do can be low. When the CLK becomes high, five capacitors of Ca, Ci, Cu, Ce, and Co can be discharged by Ia, Ii, Iu, Ie, and Io, respectively. Among Ia, Ii, Iu, Ie, and Io, if Ia is the largest amount of current, VCa is discharged by GND faster than VCi, VCu, VCe, and VCo. If VCa becomes lower than VREF, Da becomes high. As explained earlier, because VCa is the fastest falling node among the five capacitive nodes, Da can also be the fastest rising signal among Da, Di, Du, De, and Do. The fastest rising signal of Da can generate the locking pulse that can be used as the clock signal of D flip-flop circuits of FF1, FF2, FF3, FF4, and FF5. By doing so, we can decide which vowel is the best match to the voice input. The first-rising signal of Da makes Outputa high, as shown in Figure 7. The other output signals, such as Outputi, Outputu, Outpute, and Outputo, are prevented from rising from low to high by the locking pulse that is generated by the first-rising signal of Da.


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

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

Voltage waveforms of the binary memristor crossbar and winner-take-all circuits.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256962&req=5

Figure 7: Voltage waveforms of the binary memristor crossbar and winner-take-all circuits.
Mentions: The simulated waveforms of VCa, VCi, VCu, VCe, and VCo are shown in Figure 6. Here, VCa seems to be discharged by GND faster than the other capacitor nodes of VCi, VCu, VCe, and VCo. It means that the voice input matches with the vowel ‘a’ better than the other vowels. The timing diagram of important signals in Figure 4a,b is shown in Figure 7. When the CLK signal is low, all the capacitor nodes of VCa, VCi, VCu, VCe, and VCo are precharged by VDD. At this time, VCa, VCi, VCu, VCe, and VCo are higher than VREF; thus, Da, Di, Du, De, and Do can be low. When the CLK becomes high, five capacitors of Ca, Ci, Cu, Ce, and Co can be discharged by Ia, Ii, Iu, Ie, and Io, respectively. Among Ia, Ii, Iu, Ie, and Io, if Ia is the largest amount of current, VCa is discharged by GND faster than VCi, VCu, VCe, and VCo. If VCa becomes lower than VREF, Da becomes high. As explained earlier, because VCa is the fastest falling node among the five capacitive nodes, Da can also be the fastest rising signal among Da, Di, Du, De, and Do. The fastest rising signal of Da can generate the locking pulse that can be used as the clock signal of D flip-flop circuits of FF1, FF2, FF3, FF4, and FF5. By doing so, we can decide which vowel is the best match to the voice input. The first-rising signal of Da makes Outputa high, as shown in Figure 7. The other output signals, such as Outputi, Outputu, Outpute, and Outputo, are prevented from rising from low to high by the locking pulse that is generated by the first-rising signal of Da.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
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.

No MeSH data available.