论文标题
在模拟设计空间探索中，具有深厚的增强学习的信任区域方法
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration
论文作者
论文摘要
本文介绍了模拟设计空间搜索的新观点。为了最大程度地减少上市时间，这项工作比以前的艺术中定义的全球优化更好地成为限制满意度问题。我们将基于模型的代理与无模型学习形成对比，以实施信任区域策略。因此，可以通过有监督的学习来训练简单的前馈网络，而收敛性相对微不足道。实验结果证明了搜索迭代的数量级改善。此外，还容纳了对PVT条件的前所未有的考虑。在具有TSMC 5/6NM工艺的电路上，我们的方法实现了超过人类设计师的性能。此外，该框架是在工业环境中生产的。
This paper introduces new perspectives on analog design space search. To minimize the time-to-market, this endeavor better cast as constraint satisfaction problem than global optimization defined in prior arts. We incorporate model-based agents, contrasted with model-free learning, to implement a trust-region strategy. As such, simple feed-forward networks can be trained with supervised learning, where the convergence is relatively trivial. Experiment results demonstrate orders of magnitude improvement on search iterations. Additionally, the unprecedented consideration of PVT conditions are accommodated. On circuits with TSMC 5/6nm process, our method achieve performance surpassing human designers. Furthermore, this framework is in production in industrial settings.
