MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
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MLCAD: CAD机器学习研究综述主题论文
DOI：
10.1109/tcad.2021.3124762
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发表时间：
2022-10
影响因子：
2.9
通讯作者：
Martin Rapp;H. Amrouch;Yibo Lin;Bei Yu;D. Pan;M. Wolf;J. Henkel
中科院分区：
文献类型：
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作者：
Martin Rapp;H. Amrouch;Yibo Lin;Bei Yu;D. Pan;M. Wolf;J. Henkel
随着集成电路(IC)规模的不断增大，其设计和优化阶段(即计算机辅助设计，CAD)变得越来越复杂。在设计时，需要探索大的设计空间以找到满足所有规范的实现，然后优化诸如能量、面积、延迟、可靠性等指标。在运行时，需要搜索大的配置空间以找到最佳的参数集(例如，电压/频率)来进一步优化系统。这两个空间对于穷举搜索都是不可行的，通常导致启发式优化算法在设计质量和计算开销之间找到一些折衷。机器学习(ML)可以建立强大的模型，这些模型已经成功地应用于相关领域。在本次调查中，我们对ML如何用于IC的设计时和运行时优化和探索策略进行了分类。一项对已发表技术的元研究揭示了CAD中与ML一起被充分探索和未被探索的领域，以及所采用的ML算法的趋势。我们对面向CAD的ML的研究现状进行了全面的分类和总结。最后，我们总结了仍然存在的挑战和未来的开放研究方向。
Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.