  Eye tracking is becoming an increasingly important task domain in emerging
computing platforms such as Augmented/Virtual Reality (AR/VR). Today's eye
tracking system suffers from long end-to-end tracking latency and can easily
eat up half of the power budget of a mobile VR device. Most existing
optimization efforts exclusively focus on the computation pipeline by
optimizing the algorithm and/or designing dedicated accelerators while largely
ignoring the front-end of any eye tracking pipeline: the image sensor. This
paper makes a case for co-designing the imaging system with the computing
system. In particular, we propose the notion of "in-sensor sparse sampling",
whereby the pixels are drastically downsampled (by 20x) within the sensor. Such
in-sensor sampling enhances the overall tracking efficiency by significantly
reducing 1) the power consumption of the sensor readout chain and sensor-host
communication interfaces, two major power contributors, and 2) the work done on
the host, which receives and operates on far fewer pixels. With careful reuse
of existing pixel circuitry, our proposed BLISSCAM requires little hardware
augmentation to support the in-sensor operations. Our synthesis results show up
to 8.2x energy reduction and 1.4x latency reduction over existing eye tracking
pipelines.
