NeuroEye: A 54.59mW, 12200FPS Event-Driven Near-Sensor Eye-Tracking Processor with Pipelined Spatial-Temporal Spike-Streaming

This paper presents a design of an eye tracking system based on neuromorphic computing to enhance user interaction in augmented reality (AR) and virtual reality (VR) environments. Traditional methods face challenges of high computational demands and power consumption. To address these issues, we propose a fully-spike eye-tracking system that utilizes dynamic vision sensors (DVS) for asynchronous pixel-level change detection, thereby reducing data redundancy and improving temporal resolution. We proposed a pipelined processor specifically tailored for handling DVS events and Spiking Neural Network (SNN) computations. Our spatial-temporal spike-streaming architecture enables cascaded computation across all layers, achieving high energy efficiency and high frame rate in eye-tracking tasks. Implemented in a 40nm CMOS process, NeuroEye demonstrates up to 12200 frame-per-second (FPS) and 4.47uJ/frame energy efficiency with 54.59mW power consumption in post-layout evaluations.