A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs

Abstract

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.