Placement Optimization with Deep Reinforcement Learning
This addresses placement optimization for systems and chip design, but appears incremental as it applies existing deep reinforcement learning methods to this domain.
The paper tackles the placement optimization problem in systems and chip design by formulating it as a reinforcement learning problem and solving it with policy gradient optimization, reporting lessons learned from training across various problems.
Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints. In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is. We next formulate the placement problem as a reinforcement learning problem and show how this problem can be solved with policy gradient optimization. Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.