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In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
Published on: May 13, 2020
Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary
Son Ngoc Truong1, SangHak Shin2, Sang-Don Byeon3
1School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 136-702, South Korea. sontn@kookmin.ac.kr.
The twin crossbar architecture demonstrates superior statistical-variation tolerance compared to the complementary crossbar. This enhanced robustness leads to higher recognition rates for both greyscale images and alphabet characters under varying correlation conditions.
Area of Science:
- Electrical Engineering and Computer Science
- Materials Science
- Artificial Intelligence
Background:
- Crossbar architectures are fundamental in emerging memory and computing technologies.
- Statistical variations and correlations in circuit parameters significantly impact device performance and reliability.
- Understanding variation tolerance is crucial for designing robust and high-performance computing systems.
Purpose of the Study:
- To conduct a comparative analysis of statistical-variation tolerance between complementary and twin crossbar architectures.
- To evaluate the impact of varying statistical variation and correlation parameters on the recognition rates of these architectures.
- To determine which crossbar architecture exhibits superior robustness under different operational conditions.
Main Methods:
- A comparative study was performed on complementary and twin crossbar architectures.
- Circuit simulations were utilized to test the recognition rates.
- Ten greyscale images and 26 black-and-white alphabet characters were used as test datasets under varying inter-array and intra-array correlation parameters (0 and 1).
Main Results:
- The twin crossbar architecture consistently outperformed the complementary architecture in recognition rates across all tested conditions.
- For greyscale image recognition, the twin crossbar showed an average improvement of 4% (inter-array=1, intra-array=0) and 5.6% (inter-array=1, intra-array=1).
- For alphabet character recognition, the twin crossbar achieved average improvements of 4.5% (inter-array=1, intra-array=0) and 6% (inter-array=1, intra-array=1).
Conclusions:
- The twin crossbar architecture exhibits significantly higher robustness against statistical variations and correlations compared to the complementary crossbar.
- The findings suggest that the twin crossbar architecture is a more reliable choice for applications sensitive to statistical variations.
- This study provides valuable insights for the design and optimization of future crossbar-based computing systems.
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