Non-Overlapping Placement of Macro Cells based on Reinforcement Learning in Chip Design

TL;DR

This paper introduces SRLPlacer, an end-to-end reinforcement learning-based method for macro cell placement in chip design, effectively avoiding overlaps and optimizing layout performance.

Contribution

It transforms placement into a Markov decision process and integrates standard cell layout to improve macro cell placement without overlaps.

Findings

Reduces macro cell overlaps effectively.

Shortens total wire length while considering routing congestion.

Demonstrates superior performance on ISPD2005 benchmark.

Abstract

Due to the increasing complexity of chip design, existing placement methods still have many shortcomings in dealing with macro cells coverage and optimization efficiency. Aiming at the problems of layout overlap, inferior performance, and low optimization efficiency in existing chip design methods, this paper proposes an end-to-end placement method, SRLPlacer, based on reinforcement learning. First, the placement problem is transformed into a Markov decision process by establishing the coupling relationship graph model between macro cells to learn the strategy for optimizing layouts. Secondly, the whole placement process is optimized after integrating the standard cell layout. By assessing on the public benchmark ISPD2005, the proposed SRLPlacer can effectively solve the overlap problem between macro cells while considering routing congestion and shortening the total wire length to ensure routability. Codes are available at https://github.com/zhouyusd/SRLPlacer.