Algorithm Optimization and Hardware Acceleration for Machine Learning Applications on Low-energy Systems

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\u27s I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware co-design