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A hybrid evolutionary algorithm for multiobjective variation tolerant logic mapping on nanoscale crossbar architectures

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APPLIED SOFT COMPUTING 2016年 38卷 955-966页

作者： Zhong, Fugui Yuan, Bo Li, Bin Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230026 Peoples R China Univ Sci & Technol China USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230026 Peoples R China

Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation tolerant logical mapping problem is treated as a bilevel multiobjective optimization problem. Since variation mapping is an NP-complete problem, a hybrid multiobjective evolutionary algorithm is designed to solve the problem adhering to a bilevel optimization framework. The lower level optimization problem, most frequently tackled, is modeled as the min-max-weight and min-weight-gap bipartite matching (MMBM) problem, and a hungarian-based linear programming (HLP) method is proposed to solve MMBM in polynomial time. The upper level optimization problem is solved by evolutionary multiobjective optimization algorithms, where a greedy reassignment local search operator, capable of exploiting the domain knowledge and information from problem instances, is introduced to improve the efficiency of the algorithm. The numerical experiment results show the effectiveness and efficiency of proposed techniques for the variation tolerant logical mapping problem. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Nanoscale crossbar Variation tolerant logic mapping Bilevel multiobjective optimization hungarian based linear programming Hybrid evolutionary algorithm

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