Dynamic Algorithm Configuration for Global Placement

Placement is a vital step in the physical design flow of very large-scale integration (VLSI) circuits. GPU-accelerated analytical placement algorithms, such as DREAMPlace, have achieved high-quality performance with dramatic speedup. The algorithm configurations of the analytical placer have a significant impact on its convergence and final performance. However, its tuning process is difficult and time-consuming. Recently, AutoDMP tries to search for optimal static algorithm configurations using Bayesian optimization, but the performance is still limited due to its static strategy, which cannot leverage information during algorithm execution. In this paper, we propose the dynamic algorithm configuration framework for DREAMPlace (DACDMP), using reinforcement learning (RL) to learn the dynamic control policy of the most critical hyperparameter, i.e., the learning rate. Moreover, to address the insufficiency of optimization, we increase the number of optimization steps in each Lagrangian relaxation problem, thereby improving the solution’s optimality. DACDMP outperforms the current leading methods, i.e., DREAMPlace 4.0, AutoDMP, and Xplace. For example, compared to DREAMPlace 4.0, it achieves an average improvement of 2.75% in wirelength, 18.74% in worst negative slack (WNS), 44.60% in total negative slack (TNS), and 29.39% in the number of violation points on the ICCAD 2015 benchmark.