Date of Award
8-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical Engineering
Committee Chair/Advisor
Yingjie Lao
Committee Member
Deborah Kunkel
Committee Member
Yongkai Wu
Committee Member
Linke Guo
Abstract
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.
Recommended Citation
Sun, Jianchi, "Algorithm Optimization and Hardware Acceleration for Machine Learning Applications on Low-energy Systems" (2022). All Dissertations. 3145.
https://tigerprints.clemson.edu/all_dissertations/3145
Included in
Digital Circuits Commons, Systems and Communications Commons, VLSI and Circuits, Embedded and Hardware Systems Commons