Scalability and Generalization of Circuit Training for Chip Floorplanning

Summer Yue, Ebrahim M. Songhori, Joe Wenjie Jiang, Toby Boyd, Anna Goldie, Azalia Mirhoseini, Sergio Guadarrama

Abstract: Chip floorplanning is a complex task within the physical design process, with more than six decades of research dedicated to it. In a recent paper published in Nature~\citemirhoseini2021graph, a new methodology based on deep reinforcement learning was proposed that solves the floorplanning problem for advanced chip technologies with production quality results. The proposed method enables generalization, which means that the quality of placements improves as the policy is trained on a larger number of chip blocks. In this paper, we describe Circuit Training, an open-source distributed reinforcement learning framework that re-implements the proposed methodology in TensorFlow v2.x. We will explain the framework and discuss ways it can be extended to solve other important problems within physical design and more generally chip design. We also show new experimental results that demonstrate the scaling and generalization performance of Circuit Training.