I-Corps: Enabling Electronic Design using Data Intelligence

I-Corps项目更广泛的影响/商业潜力源于其数据智能方法，以增强电子设计自动化。半导体行业通过在计算设备和消费电子产品中广泛使用集成电路（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受到芯片设计复杂性的瓶颈，例如，通过大量设备计数和功能多样性来衡量，以及对计算性能和功率/能源效率的要求越来越高。先进的集成电路制造工艺是昂贵的，但有不可避免的工艺变化，使制造芯片容易出现故障。2015年，电子设计自动化（EDA）行业的收入达到78亿美元，为集成电路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更好、稳健性更好的集成电路，同时缩短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。随着集成电路的复杂性迅速增加，对智能设计工具的需求日益突出。EDA行业正处于将机器学习算法快速集成到商业IC设计流程的早期阶段。本项目所采用的学习方法大大提高了统计回归和分类的准确性，并提供了对数据底层结构的必要理解。基于重点机器学习算法，目标EDA技术可以高效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格、设计参数和制造条件之间复杂的隐藏关系。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。作为高性能并行软件设计工具，该技术将把机器学习的力量带入电子设计领域。

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.