TY - JOUR
T1 - A polarity optimization algorithm taking into account polarity conversion sequence
AU - He, Zhenxue
AU - Liu, Jia
AU - Xiao, Limin
AU - Huo, Zhisheng
AU - Wang, Xiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - The polarity conversion sequence directly determines polarity conversion efficiency and then affects polarity optimization efficiency. However, few studies have focused on the polarity conversion sequence problem of Reed-Muller (RM) circuits. In this paper, we propose a continuous Hopfield neural network (CHNN)-based polarity conversion algorithm (CHNNPCA) for Mixed Polarity RM (MPRM) circuits, which uses the CHNN to solve the best polarity conversion sequence of polarity set waiting for evaluation before converting the polarity set. Moreover, based on the CHNNPCA, a polarity optimization algorithm (POA) is proposed to improve the polarity optimization efficiency of MPRM circuits. The experimental results on MCNC benchmark circuits show that for the large-scale polarity set, the CHNNPCA is superior to the mixed polarity conversion algorithm based on the tabular technique in terms of polarity conversion efficiency. Furthermore, compared to the traditional polarity optimization algorithm neglecting polarity conversion sequence, the POA has a considerable advantage in improving polarity optimization efficiency, especially for large-scale circuits. The POA can be extended to improve the polarity optimization efficiency of fixed polarity RM circuits.
AB - The polarity conversion sequence directly determines polarity conversion efficiency and then affects polarity optimization efficiency. However, few studies have focused on the polarity conversion sequence problem of Reed-Muller (RM) circuits. In this paper, we propose a continuous Hopfield neural network (CHNN)-based polarity conversion algorithm (CHNNPCA) for Mixed Polarity RM (MPRM) circuits, which uses the CHNN to solve the best polarity conversion sequence of polarity set waiting for evaluation before converting the polarity set. Moreover, based on the CHNNPCA, a polarity optimization algorithm (POA) is proposed to improve the polarity optimization efficiency of MPRM circuits. The experimental results on MCNC benchmark circuits show that for the large-scale polarity set, the CHNNPCA is superior to the mixed polarity conversion algorithm based on the tabular technique in terms of polarity conversion efficiency. Furthermore, compared to the traditional polarity optimization algorithm neglecting polarity conversion sequence, the POA has a considerable advantage in improving polarity optimization efficiency, especially for large-scale circuits. The POA can be extended to improve the polarity optimization efficiency of fixed polarity RM circuits.
KW - Polarity conversion
KW - Reed-Muller circuits
KW - continuous hopfield neural network
KW - polarity optimization
UR - https://www.scopus.com/pages/publications/85066877079
U2 - 10.1109/ACCESS.2019.2911355
DO - 10.1109/ACCESS.2019.2911355
M3 - 文章
AN - SCOPUS:85066877079
SN - 2169-3536
VL - 7
SP - 54809
EP - 54818
JO - IEEE Access
JF - IEEE Access
M1 - 8691735
ER -