In this paper, we introduce Dreamweaver, which belongs to a new class of
auto-regressive decision-making models known as large reasoning models (LRMs).
Dreamweaver is designed to improve 3D floorplanning in electronic design
automation (EDA) via an architecture that melds advancements in
sequence-to-sequence reinforcement learning algorithms. A significant advantage
of our approach is its ability to effectively reason over large discrete action
spaces, which is essential for handling the numerous potential positions for
various functional blocks in floorplanning. Additionally, Dreamweaver
demonstrates strong performance even when trained on entirely random
trajectories, showcasing its capacity to leverage sub-optimal or non-expert
trajectories to enhance its results. This innovative approach contributes to
streamlining the integrated circuit (IC) design flow and reducing the high
computational costs typically associated with floorplanning. We evaluate its
performance against a current state-of-the-art method, highlighting notable
improvements.

Dreamweaver 是一种新型的自主决策模型，它通过结合序列到序列的增强学习算法在电子设计自动化中改进 3D 楼层规划。该方法的重要优势在于有效地处理大规模的离散行为空间，用于处理楼层规划中各个功能模块的众多潜在位置。此外，Dreamweaver 在即使在训练完全随机的轨迹的情况下也表现出良好的性能，展示了利用次优或非专家轨迹来提高结果的能力。这种创新的方法有助于简化集成电路设计流程并减少楼层规划通常伴随的高计算成本。我们将其与当前最先进的方法进行评估，并突出显示了显着的改进。