Eye tracking captures the position and movement of human eyes, enabling machines to interact with humans, especially in Augmented and Virtual Reality (AR/VR) environments. For example, foveated rendering, which depends on high-performance eye tracking, is a key technology for creating immersive experiences in AR/VR devices.

However, current eye tracking systems use bulky lens-based cameras, which pose challenges such as large form factors and high communication costs between the camera and the processor. These limitations make it difficult to achieve the high throughput needed (e.g., >240 FPS) for real-time human-machine interaction in resource-constrained AR/VR devices. The demand for integrating advanced AI algorithms, like deep neural networks, further complicates achieving high performance.

To overcome these hurdles, we are developing intelligent eye-tracking systems through a co-design of the camera, algorithms, and processor chips. Our approach aims to meet performance requirements without sacrificing accuracy. Join us as we pave the way for next-generation eye tracking solutions and drive future innovations in intelligent imaging systems.

Corresponding Publications:

• EyeCoD @ IEEE Micro Top Picks'23
• EyeCoD @ ISCA'22
• i-FlatCam @ VLSI'22
• Best Demo @ DAC'22