Limits...
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.


The forward currents and reverse currents in the matched column (a) and unmatched column (b).Vi,0 = 0 V and Vi,1 = 1 V.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4256962&req=5

Figure 5: The forward currents and reverse currents in the matched column (a) and unmatched column (b).Vi,0 = 0 V and Vi,1 = 1 V.

Mentions: In Figure 4a, we may be concerned that the reverse current through LRS and HRS may degrade the recognition rate. To elaborate on this reverse current more, we assume two cases of memristor crossbar circuit that are matched and unmatched as shown in Figure 5a,b, respectively. In Figure 5a, Vi,0 and Vi,1 are 0 and 1, respectively. These inputs match the stored memristance values of M1, M2, M3, and M4. Here, HRS means high resistance state and LRS is low resistance state. The current summation of Ia can be calculated with Ia = I2,a + I3,a − I1,a − I4,a. I2,a and I3,a are the forward currents through M2 and M3 that are LRS. I1,a and I4,a are the reverse currents through M1 and M4 that are HRS. In calculating this current summation, Ia can be expressed simply with Ia ≈ I2,a + I3,a because the reverse currents of I1,a and I4,a are much smaller than the forward currents of I2,a and I3,a. As we know, HRS is much larger than LRS; thus, we can ignore I1,a and I4,a in calculating Ia. From this explanation, we can know that the reverse current through HRS can affect Ia very little.


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

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

The forward currents and reverse currents in the matched column (a) and unmatched column (b).Vi,0 = 0 V and Vi,1 = 1 V.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: The forward currents and reverse currents in the matched column (a) and unmatched column (b).Vi,0 = 0 V and Vi,1 = 1 V.
Mentions: In Figure 4a, we may be concerned that the reverse current through LRS and HRS may degrade the recognition rate. To elaborate on this reverse current more, we assume two cases of memristor crossbar circuit that are matched and unmatched as shown in Figure 5a,b, respectively. In Figure 5a, Vi,0 and Vi,1 are 0 and 1, respectively. These inputs match the stored memristance values of M1, M2, M3, and M4. Here, HRS means high resistance state and LRS is low resistance state. The current summation of Ia can be calculated with Ia = I2,a + I3,a − I1,a − I4,a. I2,a and I3,a are the forward currents through M2 and M3 that are LRS. I1,a and I4,a are the reverse currents through M1 and M4 that are HRS. In calculating this current summation, Ia can be expressed simply with Ia ≈ I2,a + I3,a because the reverse currents of I1,a and I4,a are much smaller than the forward currents of I2,a and I3,a. As we know, HRS is much larger than LRS; thus, we can ignore I1,a and I4,a in calculating Ia. From this explanation, we can know that the reverse current through HRS can affect Ia very little.

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.