The Department of Electrical and Computer Engineering & Computer Science welcomes Yakun Sophia Shao from NVIDIA in Santa Clara, California. She will present an ECE/CS Faculty Candidate Seminar at 10:00 a.m., Monday, April 22, in 2405 Siebel Center.
"Democratizing Domain-Specific Accelerators for Next-Generation Computing"
Moore’s Law, after transforming our world for more than 50 years, has finally slowed down. As a result, the computing industry must rely on vertically integrated systems with domain-specific accelerators to sustain performance growth. However, one major obstacle of adopting specialized accelerators is the high design cost associated with accelerator design, making it infeasible to deploying a large volume and diversity of specialized accelerators in the future systems.
My research vision is to democratize domain-specific accelerators for all applications using fully-automated design flows. Towards this goal, my research spans the full stack of accelerator design, ranging from application characterization, architectural simulation, design reuse, implementation methodology, all the way down to hardware prototyping. In this talk, I will first present Aladdin, a fast and accurate architectural simulator for specialized accelerators, enabling early-stage design space exploration of domain-specific hardware. Second, I will discuss my recent work on using high-productivity hardware design methodology to build efficient and scalable accelerators for deep learning applications. I will conclude my talk by describing ongoing efforts and future directions toward building next-generation computing platforms.
Yakun Sophia Shao a Senior Research Scientist at NVIDIA. She received her Ph.D. degree in 2016 and S.M. degree in 2014 from Harvard University, working with Professor David Brooks and Professor Gu-Yeon Wei, and a B.S. degree in Electrical Engineering from Zhejiang University, China. Her research interests are in the area of computer architecture, with a special focus on domain-specific architecture, deep-learning accelerators, and high-productivity hardware design methodology. Her work has been selected as one of the TopPicks in Computer Architecture, and her Ph.D. dissertation was nominated by Harvard for ACM Doctoral Dissertation Award. She is a Siebel Scholar, an invited participant at the Rising Stars in EECS Workshop, and a recipient of the IBM Ph.D. Fellowship.