I work at Google on optimizing the TPU performance of Gemini training and serving. My research interests include AI Systems, Energy-efficient Computing, and HW/SW Co-design.

Before joining Google, I was a Google Ph.D. Fellow and a Mavis Future Faculty Fellow and I received my Ph.D. from the University of Illinois Urbana-Champaign (UIUC) in 2022. My advisor is Prof. Deming Chen and I closely work with Prof. Wen-mei Hwu and Prof. Junjun Xiong. I received my B.S. and M.S. in UESTC, Chengdu, China.

[07/22] Successfully defended my PhD Thesis

I have successfully defended my Ph.D. thesis! I am exceptionally grateful for receiving a lot of help and support from my advisors, colleagues, friends, and family over the years. My thesis Efficient AI Hardware Acceleration is available for open access.

[01/22] AutoDistill makes NLP models run efficiently on TPUv4i

Our recent collaboration with Google proposes AutoDistill, an end-to-end model distillation and model architecture exploration framework for building hardware-aware NLP pre-trained models with BERT-level accuracy but 5X fewer parameters. [Paper]

[11/21] Won the ACM Student Research Competition Winner Award

I received the 1st place winner award of the ACM Student Research Competition at ICCAD 2021 by proposing three end-to-end design flows for building efficient edge and cloud AI systems. These flows are parts of my latest research including EcoSys (published in TCAD), F-CAD (published in DAC’21), and SkyNet (published in MLSys’20).