Fugui Zhong
ABSTRACT
There exit high variations among nano-devices in nano-electronic systems, owing to the extremely small size and the bottom-up self-assembly nanofabrication process. Therefore, it is important to develop logical function mapping techniques with the consideration of variation tolerance. In this paper, the variation tolerant logical mapping (VTLM) problem is treated as a multi-objective optimization problem (MOP), a hybridization of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a problem-specific local search is presented to solve the problem. The experiment results show that with the assistance of the problem-specific local search, the presented algorithm is effective, and can find better solutions than that without the local search.
- Y. Yang, B. Yuan and B. Li. Defect and variation tolerance logic mapping for crossbar nanoarchitectures as a multi-objective problem. International conference on Information Science and Technology, pp. 1139--1142, 2011.Google Scholar
Cross Ref
- C. Tunc and M. B. Tahoori. Variation tolerant logic mapping for crossbar array nano architectures. Design Automation Conference Asia and South Pacific (ASP-DAC), pp. 855--860, 2010. Google ScholarDigital Library
- B. Yuan, X. Yao, B. Li and T. Weise. A new memetic algorithm with fitness approximation for the defect-tolerant logic mapping in crossbar-based nono-architectures. IEEE Trans. Evolutionary Computation, digital object identifier: 10.1109/TEVC.2013.2288779. 2013.Google Scholar
- D. A. Van Veldhuizen, "Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations," Ph.D. dissertation, Dept. Electr. Comput. Eng., Graduate School Eng., Air Force Instit. Technol., Wright-Patterson AFB, OH, May 1999. Google ScholarDigital Library
- C. A. Coello Coello and N. C. Cortés. Solving multi-objective optimization problems using an artificial immune system. Genet.Programming Evolvable Mach., vol. 6, no. 2, pp. 163--190, 2005. Google ScholarDigital Library
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182--197, Apr. 2002. Google ScholarDigital Library
Index Terms
- Hybridization of NSGA-II with greedy re-assignment for variation tolerant logic mapping on nano-scale crossbar architectures
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