强化学习
计算机科学
自动化
电子设计自动化
领域(数学)
计算机体系结构
国家(计算机科学)
人工智能
计算机工程
嵌入式系统
工程类
算法
数学
机械工程
纯数学
作者
Ahmet F. Budak,Zixuan Jiang,Keren Zhu,Azalia Mirhoseini,Anna Goldie,David Z. Pan
标识
DOI:10.1109/asp-dac52403.2022.9712578
摘要
Reinforcement learning (RL) algorithms have recently seen rapid advancement and adoption in the field of electronic design automation (EDA) in both academia and industry. In this paper, we first give an overview of RL and its applications in EDA. In particular, we discuss three case studies: chip macro placement, analog transistor sizing, and logic synthesis. In collaboration with Google Brain, we develop a hybrid RL and analytical mixed -size placer and achieve better results with less training time on public and proprietary benchmarks. Working with Intel, we develop an RL-inspired optimizer for analog circuit sizing, combining the strengths of deep neural networks and reinforcement learning to achieve state-of-the-art black-box optimization results. We also apply RL to the popular logic synthesis framework ABC and obtain promising results. Through these case studies, we discuss the advantages, disadvantages, opportunities, and challenges of RL in EDA.
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