MIMONet: Multi-Input Multi-Output On-Device Deep Learning

Results of MIMONet

This projects aims to generate a neural network compression techniques for on-device deep learning, and applying to real-world robots. We consider a two-phase deep compression approach consist of reducing both intra-model and inter-model redundancy.

Zexin Li
Zexin Li
Ph.D. Student of ECE

I am a Ph.D. student at the University of California, Riverside (UCR). I am fortunate to be advised by Dr. Cong Liu and working with Dr. Yinglun Zhu. I received a bachelor’s degree from the Southern University of Science and Technology (SUSTech) under the advice of Dr. Yuqun Zhang in July 2020. My research interests include but are not limited to the interdisciplinary fields of real-time embedded systems and on-device machine learning. I am actively looking for cooperation in the following topics - (1) deploying machine learning models on real-time embedded devices, (2) system-application co-optimization of machine learning systems, and (3) improving performance robustness in machine learning systems. Feel free to contact me if we share common research interests.