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Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition.

Truong SN, Shin S, Byeon SD, Song J, Mo HS, Min KS - Nanoscale Res Lett (2015)

Bottom Line: In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0.When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one.By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

View Article: PubMed Central - PubMed

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

ABSTRACT
This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

No MeSH data available.


The crossbar array architectures of binary memristors for pattern recognition. a The complementary crossbar architecture [11, 12]. b The twin crossbar architecture [10]
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Fig2: The crossbar array architectures of binary memristors for pattern recognition. a The complementary crossbar architecture [11, 12]. b The twin crossbar architecture [10]

Mentions: Figure 2a shows the complementary crossbar architecture which is composed of two memristor arrays of M+ and M− [11, 12]. The M− array in Fig. 2a is the inversion of M+ array. Here, a0 is the input to the first row in M+ array. an−1 is the input to the (n−1)th row in M+ array. g0,0 is the cross-point memristor conductance between the first row and first column in M+ array. gn−1,m−1 is the cross-point conductance between the (n−1)th row and (m−1)th column in M+ array. In binary memristors, memristor conductance can be either LRS or HRS. In Fig. 2a, LRS is represented by solid circles and HRS is represented by open circles. a’0 that is the inversion of a0 is applied to M− array. Similarly, g’0,0 is the inversion of g0,0 in M− array. y+0 is the output of the first column in M+ array and y−0 is the output of M− array. y0 (y+0 and y−0) is the amount of similarity between the input vector and the first column in the crossbar. Similarly, yj is the result of matching of the input vector with the jth column. yj can be calculated as follows [10]:Fig. 2


Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition.

Truong SN, Shin S, Byeon SD, Song J, Mo HS, Min KS - Nanoscale Res Lett (2015)

The crossbar array architectures of binary memristors for pattern recognition. a The complementary crossbar architecture [11, 12]. b The twin crossbar architecture [10]
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608951&req=5

Fig2: The crossbar array architectures of binary memristors for pattern recognition. a The complementary crossbar architecture [11, 12]. b The twin crossbar architecture [10]
Mentions: Figure 2a shows the complementary crossbar architecture which is composed of two memristor arrays of M+ and M− [11, 12]. The M− array in Fig. 2a is the inversion of M+ array. Here, a0 is the input to the first row in M+ array. an−1 is the input to the (n−1)th row in M+ array. g0,0 is the cross-point memristor conductance between the first row and first column in M+ array. gn−1,m−1 is the cross-point conductance between the (n−1)th row and (m−1)th column in M+ array. In binary memristors, memristor conductance can be either LRS or HRS. In Fig. 2a, LRS is represented by solid circles and HRS is represented by open circles. a’0 that is the inversion of a0 is applied to M− array. Similarly, g’0,0 is the inversion of g0,0 in M− array. y+0 is the output of the first column in M+ array and y−0 is the output of M− array. y0 (y+0 and y−0) is the amount of similarity between the input vector and the first column in the crossbar. Similarly, yj is the result of matching of the input vector with the jth column. yj can be calculated as follows [10]:Fig. 2

Bottom Line: In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0.When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one.By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

View Article: PubMed Central - PubMed

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

ABSTRACT
This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

No MeSH data available.