I-Corps: Enabling Electronic Design using Data Intelligence

这个i-Corps项目更广泛的影响/商业潜力源于其增强电子设计自动化的数据智能方法。半导体行业通过在计算设备和消费电子产品中应用范围极广的集成电路(IC)，为信息技术时代提供了至关重要的硬件支柱。现代集成电路开发过程的瓶颈是芯片设计的复杂性不断增加，例如以大量的器件数量和功能多样性来衡量，以及对计算性能和功率/能源效率的日益苛刻的要求。先进的IC制造工艺成本高昂，但不可避免地存在工艺差异，使制造的芯片容易出现故障。2015年，电子设计自动化(EDA)行业的收入达到78亿美元，为IC设计提供了不可或缺的工具和方法。拟议的EDA创新的潜在市场和社会影响是巨大的。这项技术可以帮助半导体和芯片设计公司开发性能和健壮性更好的集成电路，同时减少上市时间和开发成本。这个i-Corps项目展示了针对电子设计自动化的新型机器学习算法。随着集成电路复杂性的迅速增加，对智能设计工具的需求也日益突出。EDA行业正处于将机器学习算法快速集成到商业IC设计流程中的早期阶段。本项目重点介绍的学习方法大大提高了统计回归和分类的精确度，并提供了对数据基本结构的迫切需要的理解。目标EDA技术建立在聚焦机器学习算法的基础上，可以高效地处理现有芯片设计的仿真或测量性能数据，并智能地学习性能规范、设计参数和制造条件之间的复杂隐藏关系。因此，它为IC设计优化、验证和调试提供了强大的数据科学解决方案。作为高性能的并行软件设计工具，该技术将把机器学习的力量带到电子设计领域。

The broader impact/commercial potential of this I-Corps project stems from its data intelligence approach to empower electronic design automation. The semiconductor industry provides vital hardware backbone of the information technology age through an extremely wide range of integrated circuits (ICs) in computing devices and consumer electronics. Modern IC development process is bottlenecked by growing chip design complexity, e.g. measured by large device count and functionality diversity, and ever-demanding requirements on computing performance and power/energy efficiency. Advanced IC manufacturing processes are costly, and yet have unavoidable process variations, making fabricated chips susceptible to failures. With its revenue reaching $7.8 billion in 2015, the electronic design automation (EDA) industry supplies indispensable tools and methodologies that make IC design possible. The potential market and societal impact of the proposed EDA innovation is substantial. This technology can help semiconductor and chip design companies develop integrated circuits of improved performance and robustness with a reduced time-to-market and development cost.This I-Corps project demonstrates novel machine learning algorithms targeting electronic design automation. As the complexity of integrated circuits scales up rapidly, the need for smart design tools is prominent. The EDA industry is in the early phase of rapid integration of machine learning algorithms into commercial IC design flows. The learning methods focused in this project significantly improve the accuracy of statistical regression and classification over the current-state-of-the-art, and offer the much needed understanding of the underlying structure of the data. Built upon the focused machine learning algorithms, the targeted EDA technology can efficiently process simulation or measured performance data of existing chip designs, and intelligently learn the complex hidden relationships between performance specifications, design parameters, and manufacturing conditions. As a result, it offers a powerful data science solution to IC design optimization, verification, and debug. Implemented as high-performance parallel software design tools, the technology will bring the power of machine learning to the field of electronic design.