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Power Optimization for Mixed Polarity Reed-Muller Circuits Based on Multilevel Adaptive Memetic Algorithm

  • Yuhao Zhou
  • , Zhenxue He*
  • , Yan Zhang
  • , Jia Liu
  • , Tao Wang
  • , Limin Xiao
  • , Xiang Wang
  • *此作品的通讯作者
  • Hebei Agricultural University
  • Northeastern University China
  • Beijing Information Science & Technology University

科研成果: 期刊稿件文章同行评审

摘要

Power optimization can reduce heat dissipation costs and has become an important step of circuit logic synthesis. Because the power optimization for mixed polarity Reed-Muller (MPRM) circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive memetic algorithm (MAMA), which includes global exploration optimizer, local heuristic optimizer, and initial population optimizer. We use the proposed differential evolution optimization, simulated annealing optimization, and data matching algorithm to make the population evolve. Moreover, based on the proposed matrix decomposition strategy and parallel polarity conversion algorithm, we propose a power optimization approach (POA) for MPRM circuits, which searches for an MPRM circuit with a minimum power using the MAMA. Experimental results demonstrated the effectiveness and superiority of the POA in optimizing the power of MPRM circuits.

源语言英语
文章编号3510001
期刊International Journal of Intelligent Systems
2023
DOI
出版状态已出版 - 2023

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