Toward Fully Automated Machine Learning for Routability Estimator Development

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS(2024)

引用 0|浏览8
暂无评分
摘要
The rise of machine learning (ML) technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. However, manually crafting ML models remains a complex and time-consuming process because it requires extensive human expertise and tremendous engineering efforts to carefully extract features and design model architectures. In this work, we leverage automated ML techniques to automate the ML model development for routability prediction, a well-established technique that can help to guide cell placement toward routable solutions. We present an automated feature selection method to identify suitable features for model inputs. We develop a neural architecture search method to search for high-quality neural architectures without human interference. Our search method supports various operations and highly flexible connections, leading to architectures significantly different from all previous human-crafted models. Our experimental results demonstrate that our automatically generated models clearly outperform multiple representative manually crafted solutions with a superior 9.9% improvement. Moreover, compared with human-crafted models, which easily take weeks or months to develop, our efficient automated machine learning framework completes the whole model development process with only 1 day.
更多
查看译文
关键词
Automated machine learning (AutoML),neural architecture search,physical design
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要