Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Electrical Engineering

Committee Chair/Advisor

Yingjie Lao

Committee Member

Deborah Kunkel

Committee Member

Yongkai Wu

Committee Member

Linke Guo


Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware co-design.



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