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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.


a Twin crossbar circuit of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. b Twin crossbar circuit of binary memristors for recognizing 26 black-and-white alphabet characters with 8 × 8 pixels
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Fig5: a Twin crossbar circuit of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. b Twin crossbar circuit of binary memristors for recognizing 26 black-and-white alphabet characters with 8 × 8 pixels

Mentions: Figure 5a shows a block diagram of the twin crossbar architecture of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. The conceptual schematic of Fig. 5a was already shown in Fig. 2b. In Fig. 5a, as mentioned just earlier, the input is a greyscale image with 32 × 32 pixels [10]. Hence, the number of input pixels to the crossbar is 32 × 32 = 1024. Each pixel is digitized by 4 bits. Here, a0<0:3> is the 4-bit digitized inputs of a0. /a0<0:3> is the inversion of a0<0:3> [10]. In Fig. 5a, a0<0:3> is applied to the upper M+ array and /a0<0:3> is applied to the lower M+ array. The upper M+ array and the lower M+ array are identical to each other in Fig. 5a.Fig. 5


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)

a Twin crossbar circuit of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. b Twin crossbar circuit of binary memristors for recognizing 26 black-and-white alphabet characters with 8 × 8 pixels
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Related In: Results  -  Collection

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Fig5: a Twin crossbar circuit of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. b Twin crossbar circuit of binary memristors for recognizing 26 black-and-white alphabet characters with 8 × 8 pixels
Mentions: Figure 5a shows a block diagram of the twin crossbar architecture of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. The conceptual schematic of Fig. 5a was already shown in Fig. 2b. In Fig. 5a, as mentioned just earlier, the input is a greyscale image with 32 × 32 pixels [10]. Hence, the number of input pixels to the crossbar is 32 × 32 = 1024. Each pixel is digitized by 4 bits. Here, a0<0:3> is the 4-bit digitized inputs of a0. /a0<0:3> is the inversion of a0<0:3> [10]. In Fig. 5a, a0<0:3> is applied to the upper M+ array and /a0<0:3> is applied to the lower M+ array. The upper M+ array and the lower M+ array are identical to each other in Fig. 5a.Fig. 5

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.