Efficient algorithms and software for eye tracking on an embedded platform such as a smartphone

The goal of this project can be divided into three subobjectives. First, we need to propose, implement and train an accurate eye tracking model on the server, then migrate it to an embedded platform with a simple application that can run the model. Finally, we need to experiment different pruning methods for the network and possibly explore new approaches in order to improve energy efficiency while preserving other performance metrics of the model such as frame per second and accuracy. The focus of the project will be the third subobjective. Throughout the project, various network pruning methods will be explored and incorporated into the model. Some existing approaches are found in literature. This includes energy-aware pruning and layer-by-layer pruning. TO BE CONT’D

Faculty Supervisor:

Deepa Kundur

Student:

Partner:

Massachusetts Institute of Technology

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: