Global Placement

Global placement, the optimization of object or component positions within a defined space, is a crucial problem across diverse fields, aiming to minimize costs, maximize performance, and satisfy various constraints. Current research focuses on developing efficient algorithms, including reinforcement learning, diffusion models, and genetic algorithms, often enhanced by neural networks like Graph Neural Networks, to solve these complex optimization problems. These advancements are impacting diverse applications, from optimizing chip design and resource allocation in edge computing to improving robotic manipulation and even enhancing the effectiveness of counter-terrorism strategies.