Title:  Enabling Edge-Intelligence in Resource-Constrained Autonomous Systems

Committee:

Dr. Arijit Raychowdhury, ECE, Chair , Advisor

Dr. Justin Romberg, ECE

Dr. Muhannad Bakir, ECE

Dr. Hyesoon Kim, CoC

Dr. Titash Rakshit, Qualcomm

Abstract: The objective of the proposed research is to shift Machine Learning algorithms from resource-extensive server/cloud to compute-limited edge nodes by designing energy-efficient ML systems. Multiple sub-areas of research in this domain are explored for the application of drone autonomous navigation. Our principal goal is to enable the UAV to autonomously navigate using Reinforcement Learning, without incurring any additional hardware or sensor cost. Most of the light-weight UAVs are limited in their resources such as compute capabilities and on-board energy source, and the conventional state-of-the-art ML algorithms cannot be directly implemented on them. This research addresses this issue by devising energy-efficient ML algorithms, modifying existing ML algorithms, designing energy-efficient ML accelerators, and leveraging the hardware-algorithm co-design. It is concluded that energy consumption at multiple levels of hierarchy needs to be addressed by exploring algorithmic, hardware-based, and algorithm-hardware co-design approaches.