Deep Learning on Low-resource Platforms

This thesis aims to tackle the challenge of how to implement deep learning on edge devices, by addressing the main issues of the significant data storage and memory access caused by excessive parameters and intermediate results, which benefits on-device artificial intelligence applications driven by Deep learning and internet of things.