TY - GEN
T1 - A novel macro placement approach based on simulated evolution algorithm
AU - Lin, Jai Ming
AU - Deng, You Lun
AU - Yang, Ya Chu
AU - Chen, Jia Jian
AU - Chen, Yao Chieh
N1 - Funding Information:
This work was partially supported by the National Science Council of Taiwan ROC under Grant No. MOST 108-2221-E-006-147-MY2.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This paper proposes a novel approach to handle the macro placement problem, which integrates the simulated evolution algorithm and corner stitching data structure. Unlike the simulated annealing based algorithm which has to pack each macro to a contour in its representation, a macro can be placed at any empty region according to the corner stitching. Hence, even when a chip contains several preplaced macros which do not abut to boundaries, it can be easily handled by our approach. Moreover, we further apply an efficient and effective simulated evolution algorithm to refine a placement. To avoid standard cells being placed at a small region or being pushed away from related macros in the cell placement stage, our macro placement method also preserves placement areas for standard cells by expanding macros according to the design hierarchy. The experimental results show that our approach obtains better results than CP-tree and a commercial tool in term of wirelength and routability. More importantly, our methodology can complete a large test case in 6 minutes that CP-tree fails to get a result in one day, and their runtime is 659 times longer than ours even when large test cases are ignored.
AB - This paper proposes a novel approach to handle the macro placement problem, which integrates the simulated evolution algorithm and corner stitching data structure. Unlike the simulated annealing based algorithm which has to pack each macro to a contour in its representation, a macro can be placed at any empty region according to the corner stitching. Hence, even when a chip contains several preplaced macros which do not abut to boundaries, it can be easily handled by our approach. Moreover, we further apply an efficient and effective simulated evolution algorithm to refine a placement. To avoid standard cells being placed at a small region or being pushed away from related macros in the cell placement stage, our macro placement method also preserves placement areas for standard cells by expanding macros according to the design hierarchy. The experimental results show that our approach obtains better results than CP-tree and a commercial tool in term of wirelength and routability. More importantly, our methodology can complete a large test case in 6 minutes that CP-tree fails to get a result in one day, and their runtime is 659 times longer than ours even when large test cases are ignored.
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U2 - 10.1109/ICCAD45719.2019.8942168
DO - 10.1109/ICCAD45719.2019.8942168
M3 - Conference contribution
AN - SCOPUS:85077793242
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - 2019 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Digest of Technical Papers
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019
Y2 - 4 November 2019 through 7 November 2019
ER -