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 项目更广泛的影响/商业潜力源于其支持电子设计自动化的数据智能方法。半导体行业通过计算设备和消费电子产品中极其广泛的集成电路 (IC) 为信息技术时代提供了重要的硬件支柱。 现代 IC 开发流程因芯片设计复杂性不断增加而受到瓶颈，例如通过大量设备数量和功能多样性以及对计算性能和功率/能源效率的日益苛刻的要求来衡量。先进的 IC 制造工艺成本高昂，而且不可避免地存在工艺变化，使得制造出来的芯片容易出现故障。电子设计自动化 (EDA) 行业 2015 年的收入达到 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。该技术可以帮助半导体和芯片设计公司开发具有更高性能和鲁棒性的集成电路，同时缩短上市时间和开发成本。该 I-Corps 项目展示了针对电子设计自动化的新颖机器学习算法。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 设计流程中的早期阶段。该项目重点关注的学习方法显着提高了当前最先进的统计回归和分类的准确性，并提供了对数据底层结构急需的理解。基于针对性的机器学习算法，有针对性的 EDA 技术可以有效地处理现有芯片设计的仿真或测量的性能数据，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。 该技术作为高性能并行软件设计工具实施，将把机器学习的力量带入电子设计领域。