I-Corps: Enabling Electronic Design using Data Intelligence

I-Corps：使用数据智能实现电子设计

• 批准号: 1740531
• 负责人: Peng Li
• 金额: $ 5万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740531&HistoricalAwards=false
• 关键词: Corps; Enabling; Electronic; Design; using

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.

该I-Corps项目的更广泛的影响/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设备中的非常广泛的集成电路（IC）提供了信息技术时代的重要硬件骨干。 现代的IC开发过程是通过增长的芯片设计复杂性（例如通过大型设备计数和功能多样性衡量，以及对计算性能和功率/能源效率的不断要求的要求。先进的IC制造过程成本很高，但具有不可避免的过程变化，使制造的芯片容易受到故障的影响。 2015年的收入达到78亿美元，电子设计自动化（EDA）行业提供了使IC设计成为可能的必不可少的工具和方法。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通过减少的上市时间和开发成本来开发提高性能和鲁棒性的集成电路。该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扩大，对智能设计工具的需求很突出。 EDA行业正处于将机器学习算法快速整合到商业IC设计流中的早期阶段。该项目集中在该项目中的学习方法显着提高了统计回归和分类对当前状态的准确性，并提供了对数据基础结构的急需理解。在集中的机器学习算法基础上，目标EDA技术可以有效地处理现有芯片设计的模拟或测量的性能数据，并智能学习性能规范，设计参数和制造条件之间的复杂隐藏关系。结果，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。 该技术是作为高性能并行软件设计工具实施的，将把机器学习的力量带入电子设计领域。