论文标题
灵活的多目标增强芯片的学习
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
论文作者
论文摘要
最近，出现了增强学习芯片放置的成功应用。预处理的模型对于提高效率和有效性是必要的。目前，在预训练期间，固定了客观指标的权重（例如，电线，拥塞和时间）。但是，固定的型号无法产生工程师在出现不断变化的需求时所需的位置的多样性。本文提出了灵活的多目标增强学习（MORL），以使用仅使用单个预审预周化模型的推理时间变量权重来支持目标函数。我们的宏观放置结果表明，莫尔可以有效地生成多个目标的帕累托前沿。
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.
