  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.
