On edge empowered learning model optimization for industrial applications

As new neural network models continue to expand, Artificial Intelligence (AI) applications seeking more accurate results rely on larger, deeper networks and require powerful computing devices. However, numerous resource-constrained heterogeneous devices are deployed in actual industrial settings. Additionally, due to the high cost of powerful hardware and the heterogeneous hardware system provided by different manufacturers, developing a practical and industrially usable system is challenging. To address these concerns, this thesis first elucidates the use of AI technology for early data analysis in the field of safe driving, demonstrating its potential in industrial settings. Second, a cross-platform model serving framework, LiGo, is developed and verified on actual devices and scenarios to demonstrate its ability to enhance deployment flexibility and efficiency in heterogeneous edge computing.