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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 comparison of recognition rate between the complementary and twin architectures for 10 greyscale images. a Inter-array correlation = 0 and intra-array correlation = 0. b Inter-array correlation = 0 and intra-array correlation = 1. c Inter-array correlation = 1 and intra-array correlation = 0. d Inter-array correlation = 1 and intra-array correlation = 1
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Fig7: The comparison of recognition rate between the complementary and twin architectures for 10 greyscale images. a Inter-array correlation = 0 and intra-array correlation = 0. b Inter-array correlation = 0 and intra-array correlation = 1. c Inter-array correlation = 1 and intra-array correlation = 0. d Inter-array correlation = 1 and intra-array correlation = 1

Mentions: In Fig. 7, we simulated the recognition rate for the 10 greyscale images with 32 × 32 pixels. In Fig. 7a, both the inter-array and intra-array correlations are assumed 0. In this case, the two crossbar architectures show the same rate in recognizing the tested images, because two arrays are not correlated with each other and all the memristors in each array have random variation. In Fig. 7b, the inter-array correlation is 0, but the intra-array correlation is 1. It means that two arrays are not correlated with each other, but all the memristors in each array have the correlation as strong as 1. Figure 7b also shows the same recognition rate for both the complementary array and the twin one, as already shown in Fig. 7a. From Fig. 7a, b, we can think that the complementary and twin architectures show the same recognition rate if two arrays are not correlated.Fig. 7


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 comparison of recognition rate between the complementary and twin architectures for 10 greyscale images. a Inter-array correlation = 0 and intra-array correlation = 0. b Inter-array correlation = 0 and intra-array correlation = 1. c Inter-array correlation = 1 and intra-array correlation = 0. d Inter-array correlation = 1 and intra-array correlation = 1
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Fig7: The comparison of recognition rate between the complementary and twin architectures for 10 greyscale images. a Inter-array correlation = 0 and intra-array correlation = 0. b Inter-array correlation = 0 and intra-array correlation = 1. c Inter-array correlation = 1 and intra-array correlation = 0. d Inter-array correlation = 1 and intra-array correlation = 1
Mentions: In Fig. 7, we simulated the recognition rate for the 10 greyscale images with 32 × 32 pixels. In Fig. 7a, both the inter-array and intra-array correlations are assumed 0. In this case, the two crossbar architectures show the same rate in recognizing the tested images, because two arrays are not correlated with each other and all the memristors in each array have random variation. In Fig. 7b, the inter-array correlation is 0, but the intra-array correlation is 1. It means that two arrays are not correlated with each other, but all the memristors in each array have the correlation as strong as 1. Figure 7b also shows the same recognition rate for both the complementary array and the twin one, as already shown in Fig. 7a. From Fig. 7a, b, we can think that the complementary and twin architectures show the same recognition rate if two arrays are not correlated.Fig. 7

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.