  Gaze estimation is a valuable technology with numerous applications in fields
such as human-computer interaction, virtual reality, and medicine. This report
presents the implementation of a gaze estimation system using the Sony
Spresense microcontroller board and explores its performance in latency,
MAC/cycle, and power consumption. The report also provides insights into the
system's architecture, including the gaze estimation model used. Additionally,
a demonstration of the system is presented, showcasing its functionality and
performance. Our lightweight model TinyTrackerS is a mere 169Kb in size, using
85.8k parameters and runs on the Spresense platform at 3 FPS.
