Friday, February 10, 2023
3:50 – 4:50 p.m. (CST)
Dr. Jiang Hu
Dept. of Electrical and Computer Engineering
Affiliate of Computer Science Electrical and Computer Engineering
Texas A&M University
Title: “Machine Learning for EDA and EDA for Machine Learning”
- A stochastic approach to handling noisy labels in machine learning models for chip design automation
- An analytical approach to co-optimization of CNN hardware and dataflow mapping
The wave of machine learning splashes to almost every corner of the world due to its unprecedented success. The first part of this talk will be focused on how to leverage machine learning for EDA (Electronic Design Automation). Specifically, a machine learning-based early routability prediction technique will be introduced. This technique provides a stochastic approach to handling non-deterministic data labels, which may exist in other machine learning applications. In the second part, an EDA technique for ML hardware acceleration will be presented. This is the first analytical approach to CNN hardware and dataflow co-optimization, and outperforms state-of-the-art methods in terms of both solution quality and computation runtime.
Dr. Jiang Hu is a professor in the Department of Electrical and Computer Engineering at Texas A&M University. His research interests include design automation of VLSI circuits and systems, computer architecture optimization and hardware security. He has published over 240 technical papers. He received best paper awards at DAC 2001, ICCAD 2011, MICRO 2021 and ASPDAC 2023. He was the technical program chair and the general chair of the ACM International Symposium on Physical Design (ISPD) in 2011 and 2012, respectively. He was named an IEEE fellow in 2016. He will serve as the program co-chair for ACM/IEEE Workshop on Machine Learning for CAD 2023.
More on CESG Seminars: HERE
Please join on Friday, 2/10/22 at 4:10 p.m. in ETB 1020.
Zoom option: Links and PW in syllabus or email announcement.