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 design and machine learning theory; Georgia Tech provides expertise on optimization and physical design of microelectronics.
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.
|Effective start/end date
|4/15/22 → 3/31/27
- National Science Foundation: $200,000.00