  Gaze tracking is an essential component of next generation displays for
virtual reality and augmented reality applications. Traditional camera-based
gaze trackers used in next generation displays are known to be lacking in one
or multiple of the following metrics: power consumption, cost, computational
complexity, estimation accuracy, latency, and form-factor. We propose the use
of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to
traditional camera-based gaze tracking approaches while taking all of these
metrics into consideration. We begin by developing a rendering-based simulation
framework for understanding the relationship between light sources and a
virtual model eyeball. Findings from this framework are used for the placement
of LEDs and photodiodes. Our first prototype uses a neural network to obtain an
average error rate of 2.67{\deg} at 400Hz while demanding only 16mW. By
simplifying the implementation to using only LEDs, duplexed as light
transceivers, and more minimal machine learning model, namely a light-weight
supervised Gaussian process regression algorithm, we show that our second
prototype is capable of an average error rate of 1.57{\deg} at 250 Hz using 800
mW.
