Rdta

Liu, Genggeng; Zhuang, Zhen; Guo, Wenzhong; Wang, Ting-Chi

doi:10.1145/3299874.3318026

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…The track assignment phase is usually added between global routing and detailed routing to speed up detailed routing while improving the accuracy of global routing results. Existing track assignment algorithms are divided into two main categories: CFTA [21][22][23][24][25][26][27][28] and CMTA [29][30][31][32][33][34].…”

Section: Related Workmentioning

confidence: 99%

“…[30] proposed a Track Assignment Algorithm Based on Discrete Particle Swarm Optimization (DPSO-TA), which combines genetic operation and negotiation-based refining strategy. [31][32][33] considered local nets, via location, and pin access in track assignment. [34] proposed the first track router with yield-driven wire planning to optimize yield loss induced by random defects.…”

Section: Related Workmentioning

confidence: 99%

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SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

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In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

Self Cite

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“…The SMT problem is to find a minimum connection tree under a given set of pins by introducing additional Steiner points (Liu et al, 2015c). Because SMT cannot be constructed in polynomial time, how to quickly and effectively construct an SMT is a key issue to be solved in VLSI manufacturing process (Liu et al, 2015b(Liu et al, , 2019. A heuristic search algorithm has a strong ability to solve NP-hard problem (Liu et al, 2018(Liu et al, , 2020a.…”

Section: Introductionmentioning

confidence: 99%

X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution

Liu

Yang

et al. 2021

PeerJ Computer Science

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Global routing is an important link in very large scale integration (VLSI) design. As the best model of global routing, X-architecture Steiner minimal tree (XSMT) has a good performance in wire length optimization. XSMT belongs to non-Manhattan structural model, and its construction process cannot be completed in polynomial time, so the generation of XSMT is an NP hard problem. In this paper, an X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (XSMT-MoDDE) is proposed. Firstly, an effective encoding strategy, a fitness function of XSMT, and an initialization strategy of population are proposed to record the structure of XSMT, evaluate the cost of XSMT and obtain better initial particles, respectively. Secondly, elite selection and cloning strategy, multiple mutation strategies, and adaptive learning factor strategy are presented to improve the search process of discrete differential evolution algorithm. Thirdly, an effective refining strategy is proposed to further improve the quality of the final Steiner tree. Finally, the results of the comparative experiments prove that XSMT-MoDDE can get the shortest wire length so far, and achieve a better optimization degree in the larger-scale problem.

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A high-quality global routing algorithm based on hybrid topology optimization and heuristic search for data processing in MEC

Wei

Liu

et al. 2021

J Supercomput

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Rdta

Cited by 12 publications

References 12 publications

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution

A high-quality global routing algorithm based on hybrid topology optimization and heuristic search for data processing in MEC

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