A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs

被引:0
作者
Yu, Shenglu [1 ,2 ]
Du, Shimin [1 ]
Yang, Chang [1 ,2 ]
机构
[1] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China
[2] Ningbo Univ, Coll Sci & Technol, Ningbo 315300, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 07期
关键词
VLSI; floorplanning; sequence pair; deep reinforcement learning; MCNC; GSRC; PLACEMENT;
D O I
10.3390/app14072905
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In integrated circuit (IC) design, floorplanning is an important stage in obtaining the floorplan of the circuit to be designed. Floorplanning determines the performance, size, yield, and reliability of very large-scale integration circuit (VLSI) ICs. The results obtained in this step are necessary for the subsequent continuous processes of chip design. From a computational perspective, VLSI floorplanning is an NP-hard problem, making it difficult to be efficiently solved by classical optimization techniques. In this paper, we propose a deep reinforcement learning floorplanning algorithm based on sequence pairs (SP) to address the placement problem. Reinforcement learning utilizes an agent to explore the search space in sequence pairs to find the optimal solution. Experimental results on the international standard test circuit benchmarks, MCNC and GSRC, demonstrate that the proposed deep reinforcement learning floorplanning algorithm based on sequence pairs can produce a superior solution.
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页数:14
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