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.

翻译：本文提出了一种新型自回归决策模型——大型推理模型（LRM）的新成员Dreamweaver。该模型通过融合序列到序列强化学习算法的最新进展，旨在改进电子设计自动化（EDA）中的三维布局规划。我们方法的一个显著优势是能够有效处理大规模离散动作空间，这对于布局规划中众多功能模块可能位置的决策至关重要。此外，Dreamweaver即使在完全随机轨迹上训练也表现出强劲性能，展现了其利用次优或非专家轨迹提升结果的能力。这一创新方法有助于简化集成电路（IC）设计流程，并降低布局规划通常伴随的高昂计算成本。我们将其性能与当前最先进方法进行比较，结果显示出显著提升。