IUCRC Phase II North Carolina State University: Center for Advanced Electronics through Machine Learning [CAEML]

IUCRC 第二期北卡罗来纳州立大学：机器学习先进电子学中心 [CAEML]

• 批准号: 2137283
• 负责人: Paul Franzon
• 金额: $ 50万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137283&HistoricalAwards=false
• 关键词: IUCRC; Phase; II; North; Carolina

The design complexity of modern microelectronic systems, e.g., a chip with over one billion transistors, requires automated design checks and computer simulations for verifying the functionality and reliability of microelectronic components or systems prior to manufacturing. The computer memory and run time tend to increase with design complexity, and therefore it is necessary to adopt a simplified description of the system, with reduced accuracy, to complete the design process in a timely and cost-efficient manner. This project will apply machine learning to microelectronic design verification and optimization that results in reduced design cycle time, with radically improved accuracy and reliability. CAEML, the Center for Advanced Electronics through Machine Learning, will develop a behavioral, machine-learning approach to hardware modeling, emphasizing the accuracy of the end-to-end system model. Uncertainty quantification is applied to reduce reliance on design guard-banding. CAEML will research techniques for collaborative machine learning (ML), whereby multiple organizations can jointly train ML models using their proprietary design data but without releasing any confidential information. Inverse models will be demonstrated as a feasible approach to design space exploration based on specifications. CAEML includes experts on microelectronics design and machine learning theory; North Carolina State University provides expertise on design tool flows. The microelectronics industry undergirds larger vertical markets, including computing, communications, and transportation. CAEML serves the vitally important microelectronics industry with its research, workforce development, and continuing education programs. CAEML research will improve the efficiency of the design process and the quality of the final product; the first reduces costs and the second directly benefits the public. Microelectronic systems that are both secure and reliable allow government and utilities to provide critical services to the public, while low-power microelectronic systems promote environmental sustainability. CAEML provides the microelectronics industry with a diverse pool of new graduates who have excellent professional preparation. CAEML will maintain a single repository to be used for depositing and dissemination of data, documents, and code across the Center. Repository files will be stored on virtual directories that reside on an Illinois Grainger College of Engineering (GCOE) storage array. The format of the data will be documented with metadata files that provide an explanation of the exact format. The Repository will be backed up regularly, following GCOE standards and practices. Experimental data will be retained for at least three years and as governed by the policies of the institution at which the data were gathered.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代微电子系统的设计复杂性，例如，具有超过十亿个晶体管的芯片需要自动化设计检查和计算机模拟，以在制造之前验证微电子部件或系统的功能性和可靠性。随着设计的复杂性，计算机内存和运行时间往往会增加，因此有必要采用简化的系统描述，降低准确性，以及时和具有成本效益的方式完成设计过程。该项目将机器学习应用于微电子设计验证和优化，从而缩短设计周期，并从根本上提高准确性和可靠性。CAEML，通过机器学习的先进电子中心，将开发一种用于硬件建模的行为机器学习方法，强调端到端系统模型的准确性。应用不确定性量化以减少对设计保护带的依赖。CAEML将研究协作机器学习（ML）技术，多个组织可以使用其专有的设计数据联合训练ML模型，但不会发布任何机密信息。逆模型将被证明是一种可行的方法来设计基于规格的空间探索。CAEML包括微电子设计和机器学习理论方面的专家;北卡罗来纳州州立大学提供设计工具流方面的专业知识。微电子行业支撑着更大的垂直市场，包括计算、通信和运输。CAEML通过其研究、劳动力发展和继续教育计划为至关重要的微电子行业提供服务。CAEML的研究将提高设计过程的效率和最终产品的质量;第一，降低成本，第二，直接造福公众。安全可靠的微电子系统使政府和公用事业能够向公众提供关键服务，而低功率微电子系统则促进环境的可持续性。CAEML为微电子行业提供了一个多样化的新毕业生谁拥有良好的专业准备池。CAEML将维护一个单一的存储库，用于在中心内存储和分发数据、文档和代码。存储库文件将存储在位于Illinois Grainger College of Engineering（GCOE）存储阵列上的虚拟目录中。数据格式将记录在元数据文件中，元数据文件提供了确切格式的解释。将按照全球特遣队所属装备标准和做法，定期对储存库进行备份。实验数据将保留至少三年，并受数据收集机构的政策约束。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

High Dimensional Optimization for Electronic Design

• DOI: 10.1145/3551901.3556495
• 发表时间: 2022-09
• 期刊: 2022 ACM/IEEE 4th Workshop on Machine Learning for CAD (MLCAD)
• 作者: Yuejiang Wen;J. Dean;Brian A. Floyd;P. Franzon

Machine-Learning-Based Constrained Optimization of a Test Coupon Launch Using Inverse Modeling

使用逆向建模基于机器学习的测试优惠券发射约束优化

• DOI: 10.1109/epeps58208.2023.10314941
• 发表时间: 2023
• 期刊: IEEE 32th Conference on Electrical Performance of Electronic Packages and Systems (EPEPS
• 作者: Page, A.;Chen, X.
• 通讯作者: Chen, X.

Inverse Design of Embedded Inductor with Hierarchical Invertible Neural Transport Net

分层可逆神经传输网络嵌入式电感器的逆向设计

• DOI: 10.1109/epeps53828.2022.9947131
• 发表时间: 2022
• 期刊: 2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS
• 作者: Akinwande, Oluwaseyi;Bhatti, Osama Waqar;Swaminathan, Madhavan
• 通讯作者: Swaminathan, Madhavan

DepthGraphNet: Circuit Graph Isomorphism Detection via Siamese-Graph Neural Networks

• DOI: 10.1109/mlcad58807.2023.10299839
• 发表时间: 2023-09
• 期刊: 2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)
• 作者: Fin Amin;Soumyadeep Chatterjee;P. Franzon

Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library

• DOI: 10.1145/3560834.3563833
• 发表时间: 2022-11
• 期刊: Proceedings of the 2022 Workshop on Attacks and Solutions in Hardware Security
• 作者: Furkan Aydin;Aydin Aysu