  Eye tracking has become an essential human-machine interaction modality for
providing immersive experience in numerous virtual and augmented reality
(VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and
enhanced visual privacy. However, existing eye tracking systems are still
limited by their: (1) large form-factor largely due to the adopted bulky
lens-based cameras; and (2) high communication cost required between the camera
and backend processor, thus prohibiting their more extensive applications. To
this end, we propose a lensless FlatCam-based eye tracking algorithm and
accelerator co-design framework dubbed EyeCoD to enable eye tracking systems
with a much reduced form-factor and boosted system efficiency without
sacrificing the tracking accuracy, paving the way for next-generation eye
tracking solutions. On the system level, we advocate the use of lensless
FlatCams to facilitate the small form-factor need in mobile eye tracking
systems. On the algorithm level, EyeCoD integrates a predict-then-focus
pipeline that first predicts the region-of-interest (ROI) via segmentation and
then only focuses on the ROI parts to estimate gaze directions, greatly
reducing redundant computations and data movements. On the hardware level, we
further develop a dedicated accelerator that (1) integrates a novel workload
orchestration between the aforementioned segmentation and gaze estimation
models, (2) leverages intra-channel reuse opportunities for depth-wise layers,
and (3) utilizes input feature-wise partition to save activation memory size.
On-silicon measurement validates that our EyeCoD consistently reduces both the
communication and computation costs, leading to an overall system speedup of
10.95x, 3.21x, and 12.85x over CPUs, GPUs, and a prior-art eye tracking
processor called CIS-GEP, respectively, while maintaining the tracking
accuracy.
